A polarization-aware direction-of-arrival (DoA) detection scheme is conceived that leverages the intrinsic vector sensitivity of a single Rydberg atomic vapor cell to achieve quantum-enhanced angle resolution. Our core idea lies in the fact that the vector nature of an electromagnetic wave is uniquely determined by its orthogonal electric and magnetic field components, both of which can be retrieved by a single Rydberg atomic receiver via electromagnetically induced transparency (EIT)- based spectroscopy. To be specific, in the presence of a static magnetic bias field that defines a stable quantization axis, a pair of sequential EIT measurements is carried out in the same vapor cell. Firstly, the electric-field polarization angle is extracted from the Zeeman-resolved EIT spectrum associated with an electricdipole transition driven by the radio frequency (RF) field. Within the same experimental cycle, the RF field is then retuned to a magnetic-dipole resonance, producing Zeeman-resolved EIT peaks for decoding the RF magnetic-field orientation. This scheme exhibits a dual yet independent sensitivity on both angles, allowing for precise DoA reconstruction without the need for spatial diversity or phase referencing. Building on this foundation, we derive the quantum Fisher-information matrix (QFIM) and obtain a closed-form quantum Cramér-Rao bound (QCRB) for the joint estimation of polarization and orientation angles. Finally, simulation results spanning various quantum parameters validate the proposed approach and identify optimal operating regimes. With appropriately chosen polarization and magnetic-field geometries, a single vapor cell is expected to achieve sub-0.1° angle resolution at moderate RF-field driving strengths.

The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting classical wireless communications and sensing. So far, most experimental studies have aimed for the proof of physical concepts to reveal its promise, while the practical signal model of RAQR-aided wireless communications and sensing remained under-explored. Furthermore, the performance of RAQR-based wireless receivers and their advantages over classical RF receivers have not been fully characterized. To fill these gaps, we introduce the RAQR to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow. Our scheme and model provide explicit design guidance to RAQR-aided wireless systems. We next study the performance of RAQR-aided wireless systems based on our model, and compare them to classical RF receivers. The results show that Doppler broadening-free RAQRs are capable of achieving a substantial received signal-to-noise ratio (SNR) gain of over 27 decibel (dB) and 40 dB in the photon shot limit and standard quantum limit regimes, respectively.

The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation, which is a delay-Doppler (DD) domain multi-carrier (DDMC) modulation scheme based on the DD domain orthogonal pulse (DDOP), is studied. We first revisit the linear time-varying (LTV) channel model for the wireless channel, and review the conventional multi-carrier (MC) modulation schemes and their design guidelines for both linear time-invariant (LTI) and LTV channels. We then focus on the representation of the LTV channel in an equivalent sampled DD (ESDD) domain, and propose an impulse-function-based transmission strategy for the ESDD channel. Next, we take an in-depth look into the DDOP and show that it achieves orthogonality with respect to the fine time and frequency resolutions in the ESDD domain thusbehaves likean impulse function. This allows us to unveil the unique input-output relation of the resultant ODDM modulation over the ESDD channel. We point out that the conventional MC modulation design guidelines based on the Weyl-Heisenberg (WH) frame theory can be relaxed without compromising its orthogonality or violating the WH frame theory. More specifically, for a practical communication system with bandwidth and duration constraints, MC modulation signals can be designed considering so-calledlocal or sufficient (bi)orthogonality,which refers to the (bi)orthogonality among a WH subset for the MC signal within a specific bandwidth and duration. This is different from the conventional MC modulation waveform design guidelines (such as for orthogonal frequency division multiplexing and orthogonal time frequency space) based on the global (bi)orthogonality, which is the (bi)orthogonality among a WHfull setcorresponding to the MC signal occupying the entire TF domain. This novel design guideline could potentially open up opportunities for developing future waveforms required by new applications such as communication systems associated with high delay and/or Doppler shifts, as well as integrated sensing and communications.

The impact of both multiplicative and additive hardware impairments (HWIs) on multiple-input multiple-output affine frequency division multiplexing (MIMO-AFDM) systems is investigated. For small-scale MIMO-AFDM systems, a tight bit error rate (BER) upper bound associated with the maximum likelihood (ML) detector is derived. By contrast, for large-scale systems, a closed-form BER approximation associated with the linear minimum mean squared error (LMMSE) detector is presented, including realistic imperfect channel estimation scenarios. Our first key observation is that the full diversity order of a hardware-impaired AFDM system remains unaffected, which is a unique advantage. Furthermore, our analysis shows that 1) the BER results derived accurately predict the simulated ML performance in moderate-to-high signal-to-noise ratios (SNRs), while the theoretical BER curve of the LMMSE detector closely matches that of the Monte-Carlo based one. 2) MIMO-AFDM is more resilient to multiplicative distortions, such as phase noise and carrier frequency offset, compared to its orthogonal frequency division multiplexing (OFDM) counterparts. This is attributed to its inherent chirp signal characteristics; 3) MIMO-AFDM consistently achieves superior BER performance compared to conventional MIMO-OFDM systems under the same additive HWI conditions, as well as different velocity values. The latter is because MIMO-AFDM is also resilient to the additional inter-carrier interference (ICI) imposed by the nonlinear distortions of additive HWIs. In a nutshell, compared to OFDM, AFDM demonstrates stronger ICI resilience and achieves the maximum full diversity attainable gain even under HWIs, thanks to its intrinsic chirp signalling structure as well as to the beneficial spreading effect of the discrete affine Fourier transform.

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the signal-to-noise ratio (SNR) performance. The developed wireless model conforms to the classical RF framework, facilitating compatibility with established signal processing methodologies. Next, we investigate the associated distortion effects that might occur, specifically identifying the conditions under which distortion arises and demonstrating the boundaries of linear dynamic ranges. This provides critical insights into its practical implementations in wireless systems. Finally, extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that LO-dressed systems achieve a significant SNR gain of approximately 40~50 dB over conventional RF receivers in the standard quantum limit regime. This SNR head-room translates into reduced symbol error rates, enabling efficient and reliable transmission with higher-order constellations.

Integrated sensing and communication (ISAC) networks strive to deliver both high-precision target localization and high-throughput data services across the entire coverage area. In this work, we examine the fundamental trade-off between sensing and communication from the perspective of base station (BS) deployment. Furthermore, we conceive a design that simultaneously maximizes the target localization coverage, while guaranteeing the desired communication performance. In contrast to existing schemes optimized for a single target, an effective network-level approach has to ensure consistent localization accuracy throughout the entire service area. While employing time-of-flight (ToF) based localization, we first analyze the deployment problem from a localization-performance coverage perspective, aiming for minimizing the area Cramér-Rao Lower Bound (A-CRLB) to ensure uniformly high positioning accuracy across the service area. We prove that for a fixed number of BSs, uniformly scaling the service area by a factor$\kappa $increases the optimal A-CRLB in proportion to$\kappa ^{2 \beta }$, where$\beta $is the BS-to-target pathloss exponent. Based on this, we derive an approximate scaling law that links the achievable A-CRLB across the area of interest to the dimensionality of the sensing area. We also show that cooperative BSs extend the coverage but yield marginal A-CRLB improvement as the dimensionality of the sensing area grows. By exploiting the invariance properties discovered with respect to the displacement, rotation, and symmetric projection deformation, we derive a deployment-invariant structure for conceiving a low-complexity framework for ISAC network deployment. We then formulate the joint sensing-communication optimization problem and present a Majorization-Minimization algorithm for designing high-quality deployment solutions. Extensive simulations demonstrate that our framework significantly enhances sensing coverage, while maintaining the desired communication throughput.

An efficient framework is conceived for fractional matrix programming (FMP) optimization problems (OPs) namely for minimization and maximization. In each generic OP, either the objective or the constraints are functions of multiple arbitrary continuous-domain fractional functions (FFs). This ensures the framework’s versatility, enabling it to solve a broader range of OPs than classical FMP solvers, like Dinkelbach-based algorithms. Specifically, the generalized Dinkelbach algorithm can only solve multiple-ratio FMP problems. By contrast, our framework solves OPs associated with a sum or product of multiple FFs as the objective or constraint functions. Additionally, our framework provides a single-loop solution, while most FMP solvers require twin-loop algorithms. Many popular performance metrics of wireless communications are FFs. For instance, latency has a fractional structure, and minimizing the sum delay leads to an FMP problem. Moreover, the mean square error (MSE) and energy efficiency (EE) metrics have fractional structures. Thus, optimizing EE-related metrics such as the sum or geometric mean of EEs and enhancing the metrics related to spectral-versus-energy-efficiency tradeoff yield FMP problems. Furthermore, both the signal-to-interference-plus-noise ratio and the channel dispersion are FFs. In this paper, we also develop resource allocation schemes for multi-user multiple-input multiple-output (MU-MIMO) systems, using finite block length (FBL) coding, demonstrating attractive practical applications of FMP by optimizing the aforementioned metrics.

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-orthogonal multiple access (NOMA), spatial division multiple access (SDMA), and broadcasting. Another effective technology to enhance the performance of URLLC systems and mitigate interference is constituted by reconfigurable intelligent surfaces (RISs). This paper develops RSMA schemes for multi-user multiple-input multiple-output (MIMO) RIS-aided broad-cast channels (BCs) based on finite block length (FBL) coding. We show that RSMA and RISs can substantially improve the spectral efficiency (SE) and energy efficiency (EE) of MIMO RIS-aided URLLC systems. Additionally, the gain of employing RSMA and RISs noticeably increases when the reliability and latency constraints are more stringent. Furthermore, RISs impact RSMA differently, depending on the user load. If the system is underloaded, RISs are able to manage the interference sufficiently well, making the gains of RSMA small. However, when the user load is high, RISs and RSMA become synergetic.

Generalized spatial modulation-aided affine frequency division multiplexing (GSM-AFDM) is conceived for reliable multiple-input multiple-output (MIMO) communications over doubly selective channels. We commence by proposing several low-complexity detectors for large-scale GSM-AFDM systems to meet the diverse requirements of heterogeneous receiver designs in terms of detection complexity and reliability. Specifically, we introduce the linear minimum mean square error (LMMSE) equalizer-based maximum likelihood detector (LMMSE-MLD). By exploiting the GSM properties, we then derive the LMMSE-based transmit-antenna activation pattern (TAP) check-based log-likelihood ratio detector (LMMSE-TC-LLRD). In addition, we propose a pair of new detectors, namely the greedy residual check detector (GRCD) and the reduced space check detector (RSCD). We also derive a bit error rate (BER) upper-bound by considering the MLD. Our analytical results are also available for multiple-input multiple-output (MIMO)-AFDM, since MIMO-AFDM can be regarded as a special case of the proposed GSM-AFDM. Our simulation results demonstrate that 1) the BER upper bound derived is tight for moderate to high signal-to-noise ratios (SNRs), 2) the proposed GSM-AFDM achieves lower BER than its conventional orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) and AFDM counterparts. Specifically, at a BER of 10−4and a velocity of 540 km/h, the proposed GSM-AFDM is capable of attaining about 6 dB SNR gain compared to GSM-OFDM, and 3) the conceived detectors strike a compelling trade-off between the BER and complexity.

We present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a max-min rate optimization approach, which aims for maximizing the minimum rate for all users through iterative solutions of quadratic problems. To reduce complexity, we then propose a surrogate-based optimization approach that offers a low-complexity design alternative relying on closed-form updates. Our simulations show that the surrogate-based approach achieves nearly the same minimum rate as max-min optimization, while delivering sum-rates comparable to those of sum-rate maximization, overcoming the rate-fairness deficiency typical of the latter.

Flexible intelligent metasurfaces (FIMs) constitute a promising technology that could significantly boost the wireless network capacity. An FIM is essentially a soft array made up of many low-cost radiating elements that can independently emit electromagnetic signals. What's more, each element can flexibly adjust its position, even perpendicularly to the surface, to morph the overall 3D shape. In this paper, we study the potential of FIMs in point-to-point multiple-input multiple-output (MIMO) communications, where two FIMs are used as transceivers. In order to characterize the capacity limits of FIM-aided narrowband MIMO transmissions, we formulate an optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs, as well as the transmit covariance matrix, subject to a specific total transmit power constraint and to the maximum morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed. Numerical results verify that FIMs can achieve higher MIMO capacity than traditional rigid arrays. In some cases, the MIMO channel capacity can be doubled by employing FIMs.

Rydberg atomic quantum receivers exhibit great potential in assisting classical wireless communications due to their outstanding advantages in detecting radio frequency signals. To realize this potential, we integrate a Rydberg atomic quantum receiver into a classical multi-user multiple-input multiple-output (MIMO) scheme to form a multi-user Rydberg atomic quantum MIMO (RAQ-MIMO) system for the uplink. To study this system, we first construct an equivalent baseband signal model, which facilitates convenient system design, signal processing and optimizations. We then study the ergodic achievable rates under both the maximum ratio combining (MRC) and zero-forcing (ZF) schemes by deriving their tight lower bounds. We next compare the ergodic achievable rates of the RAQ-MIMO and the conventional massive MIMO schemes by offering a closed-form expression for the difference of their ergodic achievable rates, which allows us to directly compare the two systems. Our results show that RAQ-MIMO allows the average transmit power of users to be$>25 \text{d B m}$lower than that of the conventional massive MIMO. Viewed from a different perspective, an extra$\sim 8.8$bits/s/Hz/user rate becomes achievable by ZF RAQ-MIMO.

The physical layer security of a non-orthogonal multiple access (NOMA) system is investigated. In order to maximize the security level of each NOMA user in the system, a multiobjective optimization (MOO) problem is proposed for handling the relationship among conflicting objectives. A deep neural network-based framework is designed for solving the associated MOO problem and for estimating the Pareto front. The framework shows that the estimated Pareto front is very close to the true one, thus allowing designers to select Pareto optimal solutions for striking the most appropriate compromise for all users, even on dynamically time-variant basis. Numerical results are provided for illustrating the associated trade-offs.

Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base station (BS) and the passive beamforming at the RIS. Therefore, the accuracy of channel estimation is crucial for RIS-aided systems. The challenge is that only the cascaded two-hop channel spanning from the user equipments (UEs) to the RIS and spanning from the RIS to the BS can be estimated, due to the lack of active radio frequency (RF) chains at RIS elements, which leads to high pilot overhead. In this paper, we propose a low-overhead linear minimum mean square error (LMMSE) channel estimation method by exploiting the spatial correlation of channel links, which strikes a trade-off between the pilot overhead and the channel estimation accuracy. Moreover, we calculate the theoretical normalized mean square error (MSE) for our channel estimation method. Finally, we verify numerically that the proposed LMMSE estimator has lower MSE than the state-of-the-art (SoA) grouping based estimators.

This work studies a cooperative architecture for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate the allocation of antennas-to-base stations (BSs) as a means to optimize antenna densities and explore the range between massive MIMO and cell-free typologies, and their effects on cooperative sensing and cooperative communication performance. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)-based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, to comprehensively assess their effects on ISAC network performance. In networks with multiple ISAC nodes following a Poisson point process, the Cramér-Rao lower bound (CRLB) for time of flight (TOF)-based methods decreases with the square of the logarithm of the number of nodes, for angle of arrival (AOA)-based methods with the logarithm, and for hybrid methods as a mix of both. In terms of communication performance, we derive a tractable expression for the communication data rate under various cooperative region sizes. The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.

This paper investigates a two-user downlink system for integrated sensing and communication (ISAC) in which the two users deploy a fluid antenna system (FAS) and adopt the non-orthogonal multiple access (NOMA) strategy. Specifically, the integrated sensing and backscatter communication (ISABC) model is considered, where a dual-functional base station (BS) serves to communicate the two users and sense a tag’s surrounding. In contrast to conventional ISAC, the backscattering tag reflects the signals transmitted by the BS to the NOMA users and enhances their communication performance. Furthermore, the BS extracts environmental information from the same backscatter signal in the sensing stage. Firstly, we derive closed-form expressions for both the cumulative distribution function (CDF) and probability density function (PDF) of the equivalent channel at the users utilizing the moment matching method and the Gaussian copula. Then in the communication stage, we obtain closed-form expressions for both the outage probability and for the corresponding asymptotic expressions in the high signal-to-noise ratio (SNR) regime. Moreover, using numerical integration techniques such as the Gauss-Laguerre quadrature (GLQ), we have series-form expressions for the user ergodic communication rates (ECRs). In addition, we get a closed-form expression for the ergodic sensing rate (ESR) using the Cramér-Rao lower bound (CRLB). Finally, the accuracy of our analytical results is validated numerically, and we confirm the superiority of employing FAS over traditional fixed-position antenna systems in both ISAC and ISABC.

By harnessing the delay-Doppler (DD) resource domain, orthogonal time-frequency space (OTFS) substantially improves the communication performance under high-mobility scenarios by maintaining quasi-time-invariant channel characteristics. However, conventional multiple access (MA) techniques fail to efficiently support OTFS in the face of diverse communication requirements. Recently, multi-dimensional MA (MDMA) has emerged as a flexible channel access technique by elastically exploiting multi-domain resources for tailored service provision. Therefore, we conceive an elastic multi-domain resource utilization mechanism for a novel multi-user OTFS-MDMA system by leveraging user-specific channel characteristics across the DD, power, and spatial resource domains. Specifically, we divide all DD resource bins into separate subregions called DD resource slots (RSs), each of which supports a fraction of users, thus reducing the multi-user interference. Then, the most suitable MA, including orthogonal, non-orthogonal, or spatial division MA (OMA/ NOMA/ SDMA), will be selected with each RS based on the interference levels in the power and spatial domains, thus enhancing the spectrum efficiency. Then, we jointly optimize the user assignment, MA scheme selection, and power allocation in all DD RSs to maximize the weighted sum-rate subject to their minimum rate and various practical constraints. Since this results in a non-convex problem, we develop a dynamic programming and monotonic optimization (DPMO) method to find the globally optimal solution in the special case of disregarding rate constraints. Subsequently, we apply a low-complexity algorithm to find sub-optimal solutions in general cases.

Large optical reconfigurable intelligent surfaces (ORISs) are proposed for employment on building rooftops to facilitate free-space quantum key distribution (QKD) between highaltitude platforms (HAPs) and low-altitude platforms (LAPs). Due to practical constraints, the communication terminals can only be positioned beneath the LAPs, preventing direct upward links to HAPs. By deploying ORISs on rooftops to reflect the beam arriving from HAPs towards LAPs from below, reliable HAP-to-LAP links can be established. To accurately characterize the optical beam propagation, we develop an analytical channel model based on extended Huygens-Fresnel principles for representing both the atmospheric turbulence effects and the hovering fluctuations of LAPs. This model facilitates adaptive ORIS beam-width control through linear, quadratic, and focusing phase shifts, which are capable of effectively mitigating the detrimental effects of beam broadening and pointing errors (PE). Consequently, the information-theoretic bound of the secret key rate and the security performance of a decoy-state QKD protocol are analyzed. Our findings demonstrate that quadratic phase shifts enhance the SKR at high HAP-ORIS zenith angles or mild PE conditions by narrowing the beam to optimal sizes. By contrast, linear phase shifts are advantageous at low HAP-ORIS zenith angles or moderate-to-high PE by diverging the beam to mitigate LAP fluctuations.

Visible Light Positioning and Communication (VLPC) is a promising candidate for implementing Integrated Sensing And Communication (ISAC) in the unlicensed 400 THz to 800 THz band. The current Visible Light Positioning (VLP) systems mainly operate based on the Received Signal Strength (RSS) of the Line-of-Sight (LoS) path. However, its accuracy is degraded by interferences from Non-LoS (NLoS) paths. Furthermore, in Visible Light Communication (VLC) systems, the estimation of Channel State Information (CSI) also becomes challenging, when the optical channel becomes dispersive. Against this background, we propose a new VLPC scheme using Direct Current (DC) biased Optical Orthogonal Frequency-Division Multiplexing (VLPC-DCO-OFDM), where OFDM-based sensing is applied for the sake of improving the resolution of the estimated Channel Impulse Response (CIRs) exploited for positioning functionality. The CIRs estimated by sensing are further exploited to provide enhanced CSI for communication data detection. Moreover, we propose a hybrid Radar-RSS based solution, where the conventional RSS-aided VLP method is invoked for the sake of refining OFDM radar. Our simulation results demonstrate that the proposed VLPC-DCO-OFDM scheme – which simultaneously supports the triple functionalities of illumination, bi-static sensing and communication – is capable of achieving centimeter-level positioning accuracy and Giga-bits-per-second data rate.

The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to ‘morph’ its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups.

Reconfigurable holographic surfaces (RHS) are intrinsically amalgamated with reconfigurable intelligent surfaces (RIS), for beneficially ameliorating the signal propagation environment. This potent architecture significantly improves the system performance in non-line-of-sight scenarios at a low power consumption. Briefly, the RHS technology integrates ultra-thin, lightweight antennas onto the transceiver, for creating sharp, high-gain directional beams. We formulate a user sum-rate maximization problem for our RHS-RIS-based hybrid beamformer. Explicitly, we jointly design the digital, holographic, and passive beamformers for maximizing the sum-rate of all user equipment (UE). To tackle the resultant nonconvex optimization problem, we propose an alternating maximization (AM) framework for decoupling and iteratively solving the subproblems involved. Specifically, we employ the zero-forcing criterion for the digital beamformer, leverage fractional programming to determine the radiation amplitudes of the RHS and utilize the Riemannian conjugate gradient algorithm for optimizing the RIS phase shift matrix of the passive beamformer. Our simulation results demonstrate that the proposed RHS-RIS-based hybrid beamformer outperforms its conventional counterpart operating without an RIS in multi-UE scenarios. The sum-rate improvement attained ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and at the UEs, which is significant.

A three-dimensional (3D) sparse signal recovery problem formulation is conceived for delay, Doppler, and angular (DDA) domain target parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems relying on a hybrid beamforming architecture. Subsequently, a 3D-sparse Bayesian learning (3D-BL) algorithm is proposed to jointly estimate the angular, range, velocity, and radar cross-section (RCS) parameters of the targets. Furthermore, an uplink beamformer is designed for the user equipment (UE) to alleviate the complexity of uplink parameter estimation at the dual-functional radar-communication (DFRC) base station (BS) by eliminating the need for angle of departure (AoD) estimation. Additionally, a Bayesian alternating minimization (BAT-MIN) algorithm is constructed for the designing of a DFRC waveform, enabling the simultaneous generation of beams toward both the radar targets and the UE. Furthermore, the sparse Bayesian learning lower bound (SBL-LB) and the Bayesian Cramér-Rao lower bound (BCRLB) are derived to serve as benchmarks for estimation performance. Finally, simulation results are presented to showcase the enhanced performance of the proposed methodologies in terms of multiple performance metrics when contrasted both to the existing sparse recovery techniques and to conventional non-sparse parameter estimation algorithms. The simulation outcomes unequivocally demonstrate the commendable performance of the proposed 3D-BL estimation methodology, approaching closely to the SBL-LB. Notably, this approach exhibits a substantial gain of at least 5 dB when compared to alternative techniques. Additionally, the introduced BAT-MIN beamformer emerges as a highly competitive solution, closely approximating the capabilities of a fully digital beamformer while maintaining a noteworthy minimum advantage over its contemporaries. These findings underscore the significance and efficacy of the proposed techniques in the context of advanced signal processing and beamforming.

Intelligent metasurfaces may be harnessed for realizing efficient holographic multiple-input and multiple-output (MIMO) systems, at a low hardware-cost and high energy-efficiency. As part of this family, we propose a hybrid beamforming design for stacked intelligent metasurfaces (SIM) aided wideband wireless systems relying on the near-field channel model. Specifically, the holographic beamformer is designed based on configuring the phase shifts in each layer of the SIM for maximizing the sum of the baseband eigen-channel gains of all users. To optimize the SIM phase shifts, we propose a layer-by-layer iterative algorithm for optimizing the phase shifts in each layer alternately. Then, the minimum mean square error (MMSE) transmit precoding method is employed for the digital beamformer to support multi-user access. Furthermore, the mitigation of the SIM phase tuning error is also taken into account in the digital beamformer by exploiting its statistics. The power sharing ratio of each user is designed based on the iterative waterfilling power allocation algorithm. Additionally, our analytical results indicate that the spectral efficiency attained saturates in the high signal-to-noise ratio (SNR) region due to the phase tuning error resulting from the imperfect SIM hardware quality. The simulation results show that the SIM-aided holographic MIMO outperforms the state-of-the-art (SoA) single-layer holographic MIMO in terms of its achievable rate. We further demonstrate that the near-field channel model allows the SIM-based transceiver design to support multiple users, since the spatial resources represented both by the angle domain and the distance domain can be exploited.

The feasibility of continuous variable quantum key distribution (CV-QKD) is considered in the Terahertz (THz) band, experiencing time-varying and frequency-selective fading. Advanced multi-carrier modulation is required for improving the secret key rate (SKR). However, the hostile quantum channel requires powerful classical channel coding schemes for maintaining an adequate reconciliation performance. Against this background, for the first time in the open literature, we propose a multi-carrier quantum transmission regime that incorporates both orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) transmission over doubly selective fading THz channels. Furthermore, we propose a modified multi-dimensional reconciliation algorithm for CV-QKD, facilitating the integration of OFDM/OTFS quantum transmission with low-density parity check (LDPC) coded key reconciliation. Moreover, we harness multiple-input multiple-output (MIMO) beamforming for mitigating the severe THz path loss. Our SKR analysis results demonstrate that the proposed OTFS-based and LDPC-assisted CV-QKD system is capable of outperforming its OFDM counterpart in mobile wireless scenarios. Moreover, we also demonstrate that increasing the MIMO dimension reduces the transmission power required for achieving the secure transmission distance target.

Continuous-variable quantum key distribution (CV-QKD) maps information onto the quadrature components of electromagnetic waves, so that off-the-shelf wireless transceivers can be utilized. This motivates the move from optical to Terahertz (THz) bands. However, wireless THz channels suffer from severe path loss, while the mobility of wireless users imposes doubly selective fading. Against this background, we propose a new CV-QKD regime that relies on hybrid beamforming (HBF) assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) system, where the channel’s transmissivity and robustness against double selectivity are overcome by HBF and OTFS, respectively. Secondly, in order to provide channel state information (CSI) for both the transmitter (CSI-T) and receiver (CSI-R), practical channel estimation methods are conceived. They operate in the time-frequency domain for OFDM and in the delay-Doppler domain for OTFS. Thirdly, soft-decision detection is devised for our MIMO OFDM/OTFS aided multi-dimensional reconciliation (MDR) scheme. Low-density parity-check (LDPC) coding is invoked for further improving secure CV-QKD transmission distance in the THz band. Our simulation results demonstrate that the proposed HBF MIMO OTFS-based CV-QKD system relying on realistic estimated CSI is capable of achieving an adequate secret key rate (SKR) and secure transmission distance in hostile doubly selective THz channels.

Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar system in the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target-plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D) sparsity arising in the AD domain of the scattering scene and employs the powerful SBL framework for the estimation of target parameters, such as the angle-of-departure (AoD), angle-of-arrival (AoA) and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off-grid SBL framework is developed for recursively updating the parameter grid, thereby progressively refining the estimates. We also determine the Cramér-Rao bound (CRB) and Bayesian CRB for target parameter estimation in order to benchmark the estimation performance. Our simulation results corroborate the superior performance of the proposed approach in comparison to the existing algorithms, and also their ability to approach the bounds derived.

Variational Bayesian learning (VBL)-based sparse channel state information (CSI) estimation is conceived for multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) and for orthogonal time sequence multiplexing (OTSM)-based systems relying on low-resolution analog-to-digital convertors (ADCs). First, the CSI estimation model is developed for MIMO-OTFS systems considering quantized outputs. Then a novel VBL technique is developed for exploiting the inherent DD domain sparsity. Subsequently, an end-to-end system model is derived for MIMO-OTSM systems, once again, using only finite-resolution ADCs. Similar to OTFS systems, it is demonstrated that the channel is sparse in the delay-sequency (DS)-domain. Thus the sparse CSI estimation problem of the MIMO-OTSM system can also be solved using the VBL technique developed for its OTFS counterpart. A bespoke minimum mean square error (MMSE) receiver is developed for data detection, which unlike the conventional MMSE receiver also accounts for the quantization error. Finally, finite-resolution ADCs emerge as a solution, offering reduced costs and energy consumption amid the growing challenge posed by energy-intensive high-resolution ADCs in Next-Generation (NG) systems. The efficacy of the proposed techniques is validated by simulation results, surpassing the state-of-the-art and signalling a transition towards more sustainable communication technologies.

Multi-dimensional sparse channel state information (CSI) acquisition is conceived for Orthogonal time frequency space (OTFS) modulation-based millimetre wave (mmWave) multiple input and multiple output (MIMO) systems. A comprehensive end-to-end relationship is derived in the delay-Doppler (DDA) domain by additionally considering the angular parameters and a hybrid beamforming (HB) architecture. A time-domain pilot model tailored for CSI estimation (CE) in the DDA-domain is proposed, which exploits the inherent multi-dimensional (4D) sparsity that emerges in the DDA-domain during the CE process. An efficient low-complexity Bayesian learning (LC-BL) technique is conceived to fulfil the objective of CSI estimation in such systems. Subsequently, a comprehensive examination of the complexity of the algorithm under consideration is also provided. It is worth noting that the complexity of the BL scheme designed is similar to that of popular orthogonal matching pursuit (OMP), but significantly lower than that of the traditional expectation-maximization (EM) based BL technique. Moreover, a single-stage transmit precoder (TPC) and receiver combiner (RC) design is proposed. This procedure aims for maximizing the directional gain of the RF TPC/RC pair by optimizing their weights. Additionally, a series of comprehensive simulations are conducted which incorporate the use of a practical channel model and fractional Doppler shifts. In light of the inherent trade-offs between complexity and estimation algorithm performance, our proposed scheme, LC-BL, appears suitable, especially considering the substantial enhancement in the performance of CE compared to the existing benchmarks.

In this paper,..........

The spectral efficiency (SE) and global energy efficiency (GEE) trade-off encountered in the design of millimeter-wave (mmWave)-based massive multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated with a particular focus on user clustering. By exploiting the similarity among user channels a pair of spectral and energy-efficient user clustering algorithms are proposed for dynamically selecting both the number of clusters and the number of users in each cluster. Subsequently, a joint analog precoder/combiner and user clustering technique is developed, followed by a multi-objective optimization (MOO) framework for flexibly balancing the GEE and SE objectives in a mmWave NOMA network subject to specific constraints. The MOO objective is initially transformed to a weighted sum rate maximization problem, followed by a quadratic-transform (QT)-based approach conceived for maximizing the non-convex objective by approximating it as a concave-convex function. Our simulation results demonstrate that the user clustering techniques designed attain a 85% performance gain over random clustering technique and demonstrating the benefits of the algorithm designed for mmWave NOMA networks.

Pareto optimal solutions are conceived for radar beamforming error (RBE) and sum rate maximization in short-packet (SP) millimeter-wave (mmWave) integrated sensing and communication (ISAC). Our ultimate goal is to realize ultra-reliable low-latency communication (uRLLC) and real-time sensing capabilities for 6G applications. The ISAC base station (BS) transmits short packets in the downlink (DL) to serve multiple communication users (CUs) and detect multiple radar targets (RTs). We investigate the performance trade-off between the sensing and communication capabilities by optimizing both the radio frequency (RF) and the baseband (BB) transmit precoder (TPC), together with the block lengths. The optimization problem considers the minimum rate requirements of the CUs, the maximum tolerable radar beamforming error (RBE) for the RTs, the unit modulus (UM) elements of the RF TPC, and the finite transmit power as the constraints for SP transmission. The resultant problem is highly non-convex due to the intractable rate expression of the SP regime coupled with the non-convex rate and UM constraints. To solve this problem, we propose an innovative two-layer bisection search (TLBS) algorithm, wherein the RF and BB TPCs are optimized in the inner layer, followed by the block length in the outer layer. Furthermore, a pair of novel methods, namely a bisection search-based majorizer and minimizer (BMM) as well as exact penalty-based manifold optimization (EPMO) are harnessed for optimizing the RF TPC in the inner layer. Subsequently, the BB TPC and the block length are derived via second-order cone programming (SOCP) and mixed integer programming methods, respectively. Finally, our exhaustive simulation results reveal the effect of system parameters for various settings on the RBE-rate region of the SP mmWave ISAC system and demonstrate a significantly enhanced performance compared to the benchmarks.

A hybrid beamformer (HBF) is designed for integrated sensing and communication (ISAC)-aided millimeter wave (mmWave) systems. The ISAC base station (BS), relying on a limited number of radio frequency (RF) chains, supports multiple communication users (CUs) and simultaneously detects the radar target (RT). To maximize the probability of detection (PD) of the RT, and achieve rate fairness among the CUs, we formulate two problems for the optimization of the RF and baseband (BB) transmit precoders (TPCs): PD-maximization (PD-max) and geometric mean rate-maximization (GMR-max), while ensuring the quality of services (QoS) of the RT and CUs. Both problems are highly non-convex due to the intractable expressions of the PD and GMR and also due to the non-convex unity magnitude constraints imposed on each element of the RF TPC. To solve these problems, we first transform the intractable expressions into their tractable counterparts and propose a power-efficient bisection search and majorization and minimization-based alternating algorithms for the PD-max and GMR-max problems, respectively. Furthermore, both algorithms optimize the BB TPC and RF TPCs in an alternating fashion via the successive convex approximation (SCA) and penalty-based Riemannian conjugate gradient (PRCG) techniques, respectively. Specifically, in the PRCG method, we initially add all the constraints except for the unity magnitude constraint to the objective function as a penalty term and subsequently employ the RCG method for optimizing the RF TPC. Finally, we present our simulation results and compare them to the benchmarks for demonstrating the efficacy of the proposed algorithms.

Modern wireless communication systems are expected to provide improved latency and reliability. To meet these expectations, a short packet length is needed, which makes the first-order Shannon rate an inaccurate performance metric for such communication systems. A more accurate approximation of the achievable rates of finite-block-length (FBL) coding regimes is known as the normal approximation (NA). It is therefore of substantial interest to study the optimization of the FBL rate in multi-user multiple-input multiple-output (MIMO) systems, in which each user may transmit and/or receive multiple data streams. Hence, we formulate a general optimization problem for improving the spectral and energy efficiency of multi-user MIMO-aided ultra-reliable low-latency communication (URLLC) systems, which are assisted by reconfigurable intelligent surfaces (RISs). We show that an RIS is capable of substantially improving the performance of multi-user MIMO-aided URLLC systems. Moreover, the benefits of RIS increase as the packet length and/or the tolerable bit error rate are reduced. This reveals that RISs can be even more beneficial in URLLC systems for improving the FBL rates than in conventional systems approaching Shannon rates.

We propose reflection pattern modulation-aided reconfigurable intelligent surface (RPM-RIS)-assisted cell-free massive multiple-input-multiple-output (CF-mMIMO) schemes for green uplink transmission. In our RPM-RIS-assisted CF-mMIMO system, extra information is conveyed by the indices of the active RIS blocks, exploiting the joint benefits of both RIS-assisted CF-mMIMO transmission and RPM. Since only part of the RIS blocks are active, our proposed architecture strikes a flexible energy vs. spectral efficiency (SE) trade-off. We commence with introducing the system model by considering spatially correlated channels. Moreover, we conceive a channel estimation scheme subject to the linear minimum mean-square error (MMSE) constraint, yielding sufficient information for the subsequent signal processing steps. Then, upon exploiting a so-called large-scale fading decoding (LSFD) scheme, the uplink signal-to-interference-and-noise ratio (SINR) is derived based on the RIS ON/OFF statistics, where both maximum ratio (MR) and local minimum mean-square error (L-MMSE) combiners are considered. By invoking the MR combiner, the closed-form expression of the uplink SE is formulated based only on the channel statistics. Furthermore, we derive the total energy efficiency (EE) of our proposed RPM-RIS-assisted CF-mMIMO system. Additionally, we propose a chaotic sequence-based adaptive particle swarm optimization (CSA-PSO) algorithm to maximize the total EE by designing the RIS phase shifts. Specifically, the initial particle diversity is promoted by invoking chaotic sequences, and an adaptive time-varying inertia weight is developed to improve its particle search performance. Furthermore, the particle mutation and reset steps are appropriately selected to enable the algorithm to escape from local optima. Finally, our simulation results demonstrate that the proposed RPM-RIS-assisted CF-mMIMO architecture strikes an attractive SE vs. EE trade-off, while the CSA-PSO algorithm is capable of attaining a significant EE performance gain compared to conventional solutions.

The research of faster-than-Nyquist (FTN) signaling has reached a state of maturity for considering practical multipath fading channels, rather than idealized additive white Gaussian noise channels only. To overcome fast-fading multipath propagations, conventional FTN systems tend to rely on channel coding techniques for cleaning up the residual errors, rather than harnessing Doppler effect mitigation. To circumvent this limitation, we propose an adaptive transmit precoding (ATPC) method associated with FTN signaling for applications in fast-fading multipath channels. Upon leveraging real-time channel state information fed back by the receiver, ATPC updates the modulation matrix to improve resilience against Doppler frequency shifts. To mitigate the inter-block interference and multipath effect, a cyclic prefix is inserted at the beginning of each transmission frame. In addition, we employ decision-directed successive interference cancellation for alleviating the inter-symbol interference stemming from FTN signaling and multipath effects. We also analyze the theoretical bit error rate (BER) performance and a pair of closed-form BER expressions are derived for extreme channel conditions, i.e., sufficiently large number of paths and sufficiently large Doppler frequency shift. Simulation results verify the effectiveness of the proposed ATPC method and demonstrate our performance improvements over conventional schemes.

Flexible intelligent metasurface (FIM) technology shows promise in terms of enhancing both the spectral and energy efficiency of wireless networks. An FIM is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals, while flexibly adjusting its position along the direction perpendicular to the surface by a process termed as “morphing”. This is of particular interest for wireless communication systems operating at millimeter-wave and terahertz frequencies, where deep fading generally occurs within a few millimeters. Hence, in contrast to conventional rigid 2D antenna arrays, the FIM surface shape may be reconfigured to improve the channel quality by beneficial 3D morphing. In this paper, we investigate the multiuser downlink, where an FIM deployed at a base station (BS) communicates with multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS, by jointly optimizing the transmit beamforming and FIM surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint of each user as well as a constraint on the maximum FIM morphing range. To solve this problem, we first consider a simple single-user scenario and show that the optimal 3D surface shape is achieved by independently adjusting each FIM element to the position having the strongest channel gain. However, in realistic multiuser scenarios, FIM surface-shape morphing involves complex tradeoffs. To address this issue, an efficient alternating optimization method is proposed to iteratively update the FIM surface shape and the transmit beamformer to gradually reduce the transmit power. Additionally, we analyze the performance gain of the FIM, showcasing a logarithmic received power scaling law versus its maximum morphing range. Finally, simulation results show that the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays at a given data rate. The code for this paper is available athttps://github.com/JianchengAn.

Low Earth Orbit (LEO) satellite networks are capable of improving the global Internet service coverage. In this context, we propose a hybrid beamforming design for holographic metasurface based terrestrial users in multi-altitude LEO satellite networks. Firstly, the holographic beamformer is optimized by maximizing the downlink channel gain from the serving satellite to the terrestrial user. Then, the digital beamformer is designed by conceiving a minimum mean square error (MMSE) based detection algorithm for mitigating the interference arriving from other satellites. To dispense with excessive overhead of full channel state information (CSI) acquisition of all satellites, we propose a low-complexity MMSE beamforming algorithm that only relies on the distribution of the LEO satellite constellation harnessing stochastic geometry, which can achieve comparable throughput to that of the algorithm based on the full CSI in the case of a dense LEO satellite deployment. Furthermore, it outperforms the maximum ratio combining (MRC) algorithm, thanks to its inter-satellite interference mitigation capacity. The simulation results show that our proposed holographic metasurface based hybrid beamforming architecture is capable of outperforming the state-of-the-art antenna array architecture in terms of its throughput, given the same physical size of the transceivers. Moreover, we demonstrate that the beamforming performance attained can be substantially improved by taking into account the mutual coupling effect, imposed by the dense placement of the holographic metasurface elements.

A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas in the massive multiple input multiple output (MIMO) fashion, which enhance beamforming and coherent processing, against those of geographically distributed antennas towards cell-free transmission, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)- based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks havingNISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follows a ln2Nscaling law (explicitly, the Cramér-Rao lower bound (CRLB) reduces with ln2N). The AOA-based methods follow a lnNscaling law, while the hybrid methods scale asaln2N+blnN, whereaandbrepresent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. Specifically, when converting AOA measurements to the target location, the localization error introduced during this conversion is inversely proportional to the distance between the BS and the target, leading to a more significant reduction in accuracy as the number of transceivers increases. In contrast, TOF-based localization avoids such distance dependent errors in the conversion process. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain. Simulations confirm that cooperative transmission and sensing in ISAC networks can effectively improve non-cooperative sensing and communication performance The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.

Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the derived Cramér-Rao lower bound (CRLB) expression of the localization accuracy unveils a significant finding: DeployingNISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the$\ln ^{2}N$scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of$N^{2}$achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance. Simulation results demonstrate that compared to the conventional time-sharing scheme or a non-cooperative scheme, the proposed cooperative ISAC scheme can effectively improve the average data rate and reduce the CRLB, hence striking an improved S&C performance tradeoff at the network level.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems are investigated under spatially correlated fading channels using realistic imperfect hardware. Specifically, the transceiver distortions, time-varying phase noise, and RIS phase shift errors are considered. Upon considering imperfect hardware and pilot contamination, we derive a linear minimum mean-square error (MMSE) criterion-based cascaded channel estimator. Moreover, a closed-form expression of the downlink ergodic spectral efficiency (SE) is derived based on maximum ratio (MR) based transmit precoding and channel statistics, where both a finite number of access points (APs) and STAR-RIS elements as well as imperfect hardware are considered. Furthermore, by exploiting the ergodic signal-to-interference-plus-noise ratios (SINRs) among user equipment (UE), a max-min fairness problem is formulated for the joint optimization of the passive transmitting and reflecting beamforming (BF) at the STAR-RIS as well as of the power control coefficients. An alternating optimization (AO) algorithm is proposed for solving the resultant problems, where iterative adaptive particle swarm optimization (APSO) and bisection methods are proposed for circumventing the non-convexity of the RIS passive BF and the quasi-concave power control sub-problems, respectively. Our simulation results illustrate that the STAR-RIS-aided CF-mMIMO system attains higher SE than its RIS-aided counterpart. The performance of different hardware parameters is also evaluated. Additionally, it is demonstrated that the SE of the worst UE can be significantly improved by exploiting the proposed AO-based algorithm compared to conventional solutions associated with random passive BF and equal-power scenarios.

A flexible intelligent metasurface (FIM) is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals and flexibly adjust its position through a 3D surface-morphing process. In our system, an FIM is deployed at a base station (BS) that transmits to multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS by jointly optimizing the transmit beamforming and the FIM’s surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint for each user as well as to a constraint on the maximum morphing range of the FIM. To address this problem, an efficient alternating optimization method is proposed to iteratively update the FIM’s surface shape and the transmit beamformer to gradually reduce the transmit power. Finally, our simulation results show that at a given data rate the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays.

Staked intelligent metasurface (SIM) based techniques are developed to perform two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array automatically performs the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. To arrange for the SIM to carry out this task, we design a gradient descent algorithm for iteratively updating the phase shift of each meta-atom in the SIM to minimize the fitting error between the SIM's response and the 2D DFT matrix. To further improve the DOA estimation accuracy, we configure the phase shifts in the input layer of the SIM to generate a set of 2D DFT matrices having orthogonal spatial frequency bins. Extensive numerical simulations verify the capability of a well-trained SIM to perform the 2D DFT. Specifically, it is demonstrated that a SIM having an optical computational speed achieves an MSE of 10–4in 2D DOA estimation.

Benders' decomposition (BD) algorithm constitutes a powerful mathematical programming method of solving mixed-integer linear programming (MILP) problems with a specific block structure. Nevertheless, BD still needs to solve an NP-hard quasi-integer programming master problem (MAP), which motivates us to harness the popular variational quantum algorithm (VQA) to assist BD. More specifically, we choose the popular quantum approximate optimization algorithm (QAOA) of the VQA family. We transfer the BD's MAP into a digital quantum circuit associated with a physically tangible problem-specific ansatz; and then solve it with the aid of a state-of-the-art digital quantum computer. Next, we evaluate the computational results and discuss the feasibility of the proposed algorithm. The hybrid approach advocated, which utilizes both classical and digital quantum computers, is capable of tackling many practical MILP problems in communication and networking, as demonstrated by a pair of case studies.

An orthogonal time sequency multiplexing (OTSM) scheme using practical signaling functions is proposed under strong phase noise (PHN) scenarios. By utilizing the transform relationships between the delay-sequency (DS), time-frequency (TF) and time-domains, we first conceive the DS-domain input-output relationship of our OTSM system, where the conventional zero-padding is discarded to increase the spectral efficiency. Then, the unconditional pairwise error probability is derived, followed by deriving the bit error ratio (BER) upper bound in closed-form. Moreover, we compare the BER performance of our OTSM system based on several practical signaling functions. Our simulation results demonstrate that the upper bound derived accurately predicts the BER performance in the case of moderate to high signal-to-noise ratios (SNRs), while harnessing practical window functions is capable of attaining an attractive out-of-band emission (OOBE) vs. BER trade-off.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for improving the spectral- and energy-efficiency of millimeter-wave (mmWave) Internet of Vehicles (IoV) communications, but the conception of their accurate channel estimation poses. Hence, the existing estimation methods mainly focus on time-invariant channels, while ignoring the Doppler effect induced by the high-velocity vehicles, which will lead to significant performance degradation. In this article, we investigate the problem of channel estimation in RIS-aided mmWave IoV systems considering the deleterious Doppler effect. First, we derive the expression of the time-varying cascaded two-hop multiple-path channels, where each delay tap is subject to multiple paths instead of having a simple one-to-one correspondence. In order to decouple the paths, the problem is formulated in the delay-domain by a series of transformations and the cascaded two-hop channel can be estimated at each delay tap. Then, we propose a pair of estimation strategies by considering different hardware constraints depending on the number of receiver antennas at the base station (BS). When a large receiver array is employed at the BS, we can exploit its high angular selectivity for distinguishing each resolvable path at a certain delay tap because they arrive from different directions. However, this cannot be achieved for small arrays, given their more limited angular resolution. Thus, the RIS reflection patterns are delicately designed for distinguishing multiple resolvable paths. After separating the paths, Doppler estimation can be performed by calculating the phase difference of the adjacent symbols. Our simulation results demonstrate the superior performance of the proposed methods within a wide range of Doppler shifts.

Given the popularity of flawless telepresence and the resultants explosive growth of wireless video applications, besides handling the traffic surge, satisfying the demanding user requirements for video qualities has become another important goal of network operators. Inspired by this, cooperative edge caching intrinsically amalgamated with scalable video coding is investigated. Explicitly, the concept of a Pareto-optimal semi-distributed multiagent multipolicy deep reinforcement learning (SD-MAMP-DRL) algorithm is conceived for managing the cooperation of heterogeneous network nodes. To elaborate, a multipolicy reinforcement learning algorithm is proposed for finding the Pareto-optimal policies during the training phase, which balances the teletraffic versus the user experience tradeoff. Then the optimal policy/solution can be activated during the execution phase by appropriately selecting the associated weighting coefficient according to the dynamically fluctuating network traffic load. Our experimental results show that the proposed SD-MAMP- acrshort DRL algorithm: 1) achieves better performance than the benchmark algorithms and 2) obtains a near-complete Pareto front in various scenarios and selects the optimal solution by adaptively adjusting the above-mentioned pair of objectives.

An integrated sensing and communication (ISAC) system is investigated, where the base station (BS) provides both uplink and downlink Internet of Things (IoT) services as well as target sensing services. Furthermore, nonorthogonal transmission (NO-T) is introduced for improving the spectrum efficiency. The deleterious effects of hardware impairments, channel estimation errors, and imperfect successive interference cancellation are taken into account. Both the exact and asymptotic outage probabilities (OPs) of the IoT devices as well as the Probability of successful Detection (PoD) are derived for characterizing the communication and sensing (C&S) performances. As a further development, in the presence of the sensing requirements, a communication-centric power allocation (PA) problem is formulated for maximizing the sum rate of the IoT devices. Given the nonconvexity of the problem, an alternating optimization algorithm is developed for finding a near-optimal PA. The simulation results confirm the accuracy of the analysis and demonstrate that: 1) the above nonideal factors degrade the C&S performances; 2) the NO-T ISAC system considered outperforms pure ISAC in terms of both its OP and PoD; and 3) compared to other baseline PA schemes, the proposed algorithm maximizes the sum rates while meeting the sensing requirements.

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, physical-layer system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S&C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution physical-layer ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input–multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimation of a parameter vector. The proposed techniques exploit the limited scattering nature of the mmWave MIMO channel for formulating the hybrid transceiver design framework as a multiple measurement vectors (MMVs)-based sparse signal recovery problem. This is then solved using the iterative appealingly low-complexity simultaneous orthogonal matching pursuit (SOMP). Tailor-made designs are presented for WSNs operating under both total and per-sensor power constraints, while considering ideal noiseless as well as realistic noisy sensors. Furthermore, both the Bayesian Cramer–Rao lower bound and the centralized MMSE bound are derived for benchmarking the proposed decentralized estimation schemes. Our simulation results demonstrate the efficiency of the designs advocated and verify the analytical findings.

A cell-free massive multiple-input-multiple-output (CF-mMIMO) system is considered for enhancing the monitoring performance of wireless surveillance, where a large number of distributed multiantenna aided legitimate monitoring nodes (MNs) proactively monitor multiple distributed untrusted communication links. We consider two types of MNs whose task is to either observe the untrusted transmitters or jam the untrusted receivers. We first analyze the performance of CF-mMIMO surveillance relying on both maximum ratio (MR) and partial zero-forcing (PZF) combining schemes and derive closed-form expressions for the monitoring success probability (MSP) of the MNs. We then propose a joint optimization technique that designs the MN mode assignment, power control, and MN-weighting coefficient control to enhance the MSP based on the long-term statistical channel state information knowledge. This challenging problem is effectively transformed into tractable forms and efficient algorithms are proposed for solving them. Numerical results show that our proposed CF-mMIMO surveillance system considerably improves the monitoring performance with respect to a full-duplex co-located massive multiple-input-multiple-output (MIMO) proactive monitoring system. More particularly, when the untrusted pairs are distributed over a wide area and use the MR combining, the proposed solution provides nearly a thirty-fold improvement in the minimum MSP over the co-located massive MIMO baseline, and forty-fold improvement, when the PZF combining is employed.

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate agility and mobility of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an -multiple objective genetic algorithm (-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering 45.38% more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering 26.86% more data than the benchmark solutions when no stationary relay is available.

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space–air–ground-integrated network (SAGIN) with diverse resource constraints. In this article, we dynamically consider three typical classes of radio access network (RAN) slices, namely, high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay, and the coverage area of these three classes of RAN slices are jointly optimized in a nonscalar form by considering the distinct channel features and service advantages of the terrestrial, aerial, and satellite components of acrshortpl SAGIN. A joint central and distributed multiagent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto-optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the interslice subchannel and power sharing by relying on a centralized unit. Then, it optimizes the intraslice subchannel and power allocation, and the virtual base station (vBS)/vUAV/virtual low Earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.

Stacked intelligent metasurfaces (SIMs) are capable of emulating reconfigurable physical neural networks by utilizing electromagnetic (EM) waves as carriers. They can also perform various complex computational and signal processing tasks. An SIM is constructed by densely integrating multiple metasurface layers, each consisting of a large number of small meta-atoms that can control the EM waves passing through it. In this paper, we harness an SIM for two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array can be designed to automatically compute the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. As a result, a receiver array can directly observe the angular spectrum of the incoming signal, and it can estimate the DOA by simply using probes to detect the energy distribution on the receiver array. This avoids the need for power inefficient radio frequency chains. To enable an SIM to perform the 2D DFT in the wave domain, we formulate an optimization problem that minimizes the mean square error (MSE) between the SIM’s EM response and the 2D DFT matrix. Then, a gradient descent algorithm is customized for iteratively updating the phase shift applied by each meta-atom of the SIM. To further improve the DOA estimation accuracy, we configure the phase shifts of the input layer of the SIM to generate a set of 2D DFT matrices associated with orthogonal spatial frequency bins. Additionally, we analytically evaluate the performance of the proposed SIM-based DOA estimator by deriving a tight upper bound for the MSE. Extensive numerical simulations verify the capability of an optimized SIM to perform DOA estimation and corroborate the theoretical analysis. Specifically, we show that an SIM is capable of performing DOA estimation with an MSE of the order of$10^{-4}$.

We demonstrate that the proposed optical orthogonal time frequency space (O-OTFS) is capable of improving the bandwidth-/power-/energy-efficiencies of optical orthogonal frequency-division multiplexing (O-OFDM). The bandwidth-efficiency is improved because only a single cyclic prefix (CP) is needed for an entire O-OTFS frame. The power-efficiency is enhanced thanks to the diversity gain achieved by its symplectic finite Fourier transform (SFFT), which also leads to a reduced peak-to-average power ratio (PAPR), hence improving its energy-efficiency. These features are facilitated by the proposed layered asymmetrically clipped O-OTFS (LACO-OTFS), which is capable of removing the direct current (DC) bias while retaining the full optical throughput. Nonetheless, there exists an inherent trade-off, where increasing the O-OTFS frame size leads to a commensurately reduced CP percentage at the cost of an increased PAPR. In order to mitigate this, we propose to perform discrete Fourier transform based spreading (DFT-S) in the delay-Doppler (DD)-domain. Furthermore, we demonstrate that regardless of the choice of domain in which the information is modulated (i.e. O-OFDM/O-OTFS with/without DFT-S), the frequency-selectivity of the quasi-static but dispersive optical channel can always be equalized by single-tap frequency-domain equalization (FDE). Moreover, the channel estimation techniques are conceived to operate in the time-/frequency-/DD-domains for both O-OFDM and O-OTFS. Our simulation results demonstrate that for a multi-user optical wireless system associated withM= 64 subcarriers and the OTFS frame length ofN= 64, LACO-OTFS is capable of achieving a 7 dB power-efficiency gain over LACO-OFDM, where the CP overhead is reduced by a factor ofN= 64. DFT-S-LACO-OTFS is also capable of providing a 7 dB power-efficiency gain over DFT-S-LACO-OFDM, where the low PAPR of single-carrier transmission is retained.

This is the first investigation focused on delivering multi-stream information to multiple multi-antenna users employing an active reconfigurable intelligent surface (aRIS)-assisted system. We conceive the joint design of the transmit precoders and of the aRIS’s power-amplified reconfigurable elements (APRES) to enhance the log-det rate objective functions for all users, which poses large-scale mixed discrete continuous problems. We develop a max-min log-det solver, which iterates quadratic-solvers of cubic complexity to maximize the nonsmooth function representing the minimum of the users’ log-det rate functions. To mitigate the computational burden associated with cubically escalating complexity in large-scale scenarios, we introduce a pair of alternative problems aimed at maximizing the smooth functions representing the sum of the users’ log-det rate function (sum log-det) and the soft minimum of the users’ log-det rate function (soft min log-det). We develop sum log-det and soft max-min solvers, leveraging closed-form expressions of scalable (linear) complexity for efficient computation. This approach ensures practicality in addressing large-scale scenarios. Furthermore, the soft min log-det enables us to enhance the log-det rates for all users and their sum, ultimately improving the quality of delivering multi-user multi-stream information.

Bayesian learning aided massive antenna array based THz MIMO systems are designed forspatial-widebandandfrequency-widebandscenarios, collectively termed as thedual-widebandchannels. Essentially, numerous antenna modules of the THz system result in a significant delay in the transmission/ reception of signals in the time-domain across the antennas, which leads to spatial-selectivity. As a further phenomenon, the wide bandwidth of THz communication results in substantial variation of the effective angle of arrival/ departure (AoA/ AoD) with respect to the subcarrier frequency. This is termed as thebeam squint effect, which renders the channel state information (CSI) estimation challenging in such systems. To address this problem, initially, a pilot-aided (PA) Bayesian learning (PA-BL) framework is derived for the estimation of the Terahertz (THz) MIMO channel that relies exclusively on the pilot beams transmitted. Since the framework designed can successfully operate in an ill-posed model, it can verifiably lead to reduced pilot transmissions in comparison to conventional methodologies. The above paradigm is subsequently extended to additionally incorporate data symbols to derive a Data-Aided (DA) BL approach that performs joint data detection and CSI estimation. We will demonstrate that it is capable of improving the dual-wideband channel’s estimate, despite further reducing the training overhead. The Bayesian Cramér-Rao bounds (BCRLBs) are also obtained for explicitly characterizing the lower bounds on the mean squared error (MSE) of the PA-BL and DA-BL frameworks. Our simulation results show the improved normalized MSE (NMSE) and bit-error rate (BER) performance of the proposed estimation schemes and confirm that they approach their respective BCRLB benchmarks.

Cooperative hybrid transmit precoder (TP) and receive combiner (RC) design algorithms are conceived for cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) networks, operating in the face of asynchronous interference (ASI). To begin with, a Wiener filtering-based optimal hybrid TP/RC (WHB-U) design is proposed for unicast scenarios that minimizes the normalized mean squared error (NMSE) between the received signal and the desired signal subject to user-specific power constraints. Next, a signal-to-leakage plus noise ratio (SLNR) maximization-based hybrid TP/RC design (SHB-U) is conceived, which reduces the interference engendered by the signal transmission targeted towards a specific user, rather than focuses on the interference at a particular user. Next, a multicast transmission scenario is considered, wherein the users belonging to a particular multicast group request identical information. Toward this, the WHB-M and SHB-M hybrid TP/RC schemes are designed for mitigating both the inter-user and inter-group interference. Subsequently, we also develop a Bayesian learning (BL)-based framework for jointly designing the RF and baseband (BB) TPs/RCs for both unicast and multicast scenarios, which does not require the full knowledge of mmWave MIMO channel components. Finally, the efficiency of the proposed TP/RC schemes is extensively evaluated by simulations both in terms of their ability to mitigate the ASI, and the spectral efficiency attained.

In contrast to the conventional reconfigurable intelligent surfaces (RIS), intelligent omni-surfaces (IOS) are capable of full-space coverage of smart radio environments by simultaneously transmitting and reflecting the incident signals. In this paper, we investigate the ergodic spectral efficiency of IOS-aided systems for transmission over random channel links, while considering both realistic imperfect channel state information (CSI) and transceiver hardware impairments (HWIs). Firstly, we formulate the linear minimum mean square error estimator of the equivalent channel spanning from the user equipments (UEs) to the access point (AP), where the transceiver HWIs are also considered. Then, we apply a two-timescale protocol for designing the beamformer of the IOS-aided system. Specifically, for the active AP beamformer, the minimum mean square error combining method is employed, which relies on the estimated equivalent channels, on the statistical information of the channel estimation error, on the inter-user interference as well as on the HWIs at the AP and UEs. By contrast, the passive IOS beamformer is designed based on the statistical CSI for maximizing the upper bound of the ergodic spectral efficiency. The theoretical analysis and simulation results show that the transceiver HWIs have a significant effect on the ergodic spectral efficiency, especially in the high transmit power region. Furthermore, we show that the HWIs at the AP can be effectively compensated by deploying more AP antennas.

Reconfigurable holographic surfaces (RHSs) constitute a promising technique of supporting energy-efficient communications. In this paper, we formulate the energy efficiency maximization problem of the switch-controlled RHS-aided beamforming architecture by alternately optimizing the holographic beamformer at the RHS, the digital beamformer, the total transmit power and the power sharing ratio of each user. Specifically, to deal with this challenging non-convex optimization problem, we decouple it into three sub-problems. Firstly, the coefficients of RHS elements responsible for the holographic beamformer are optimized to maximize the sum of the eigen-channel gains of all users by our proposed low-complexity eigen-decomposition (ED) method. Then, the digital beamformer is designed by the singular value decomposition (SVD) method to support multi-user information transfer. Finally, the total transmit power and the power sharing ratio are alternately optimized, while considering the effect of transceiver hardware impairments (HWI). We theoretically derive the spectral efficiency and energy efficiency performance upper bound for the RHS-based beamforming architectures in the presence of HWIs. Our simulation results show that the switch-controlled RHS-aided beamforming architecture achieves higher energy efficiency than the conventional fully digital beamformer and the hybrid beamformer based on phase shift arrays (PSA). Moreover, considering the effect of HWI in the beamforming design can bring about further energy efficiency enhancements.

Large-scale multiple-input and multiple-output (MIMO) systems are capable of achieving high date rate. However, given the high hardware cost and excessive power consumption of massive MIMO systems, as a remedy, intelligent metasurfaces have been designed for efficient holographic MIMO (HMIMO) systems. In this paper, we propose a HMIMO architecture based on stacked intelligent metasurfaces (SIM) for the uplink of cell-free systems, where the SIM is employed at the access points (APs) for improving the spectral- and energy-efficiency. Specifically, we conceive distributed beamforming for SIM-assisted cell-free networks, where both the SIM coefficients and the local receiver combiner vectors of each AP are optimized based on the local channel state information (CSI) for the local detection of each user equipment (UE) information. Afterward, the central processing unit (CPU) fuses the local detections gleaned from all APs to detect the aggregate multi-user signal. Specifically, to design the SIM coefficients and the combining vectors of the APs, a low-complexity layer-by-layer iterative optimization algorithm is proposed for maximizing the equivalent gain of the channel spanning from the UEs to the APs. At the CPU, the weight vector used for combining the local detections from all APs is designed based on the minimum mean square error (MMSE) criterion, where the hardware impairments (HWIs) are also taken into consideration based on their statistics. The simulation results show that the SIM-based HMIMO outperforms the conventional single-layer HMIMO in terms of the achievable rate. We demonstrate that both the HWI of the radio frequency (RF) chains at the APs and the UEs limit the achievable rate in the high signal-to-noise-ratio (SNR) region.

Online Bayesian learning-assisted channel state information (CSI) estimation schemes are conceived for single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) modulated systems. To begin with, an end-to-end system model is derived in the delay-Doppler (DD)-domain, followed by an online CSI estimation (CE) framework for SISO-OTFS systems. Next, the sequential minimum mean square error (MMSE) estimator is derived for this model which utilizes expectation maximization (EM) based sparse Bayesian learning (SBL) for initialization of the online estimation procedure. Additionally, a low-complexity detection technique is developed for the system under consideration, which is accomplished via an analogous time-frequency (TF)-domain system model that leads to a block-diagonal TF-domain channel matrix. The paradigm designed for online CE is subsequently extended to MIMO-OTFS systems. The corresponding DD-domain CSI is shown to be simultaneously row and group sparse. Hence a novel EM-based row and group sparse Bayesian learning scheme is developed for determining the initialization parameters for the above online algorithm. As a further continuation, a low-complexity detector is also proposed for MIMO-OTFS systems based on an iterative block matrix inversion technique. Furthermore, time-recursive Bayesian Cramer-Rao lower bounds (BCRLBs) are derived to benchmark the MSE performance of the proposed schemes for both the systems. Finally, simulation results are presented to demonstrate the efficiency of the proposed online estimation techniques.

The joint uplink/downlink (JUD) design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is conceived in support of both uplink (UL) and downlink (DL) users. Furthermore, the dual STAR-RISs (D-STAR) concept is conceived as a promising architecture for 360-degree full-plane service coverage, including UL/DL users located between the base station (BS) and the D-STAR as well as beyond. The corresponding regions are termed as primary (P) and secondary (S) regions. Both BS/users exist in the P-region, but only users are located in the S-region. The primary STAR-RIS (STAR-P) plays an important role in terms of tackling the P-region inter-user interference, the self-interference (SI) from the BS and from the reflective as well as refractive UL users imposed on the DL receiver. By contrast, the secondary STAR-RIS (STAR-S) aims for mitigating the S-region interferences. The non-linear and non-convex rate-maximization problem formulated is solved by alternating optimization amongst the decomposed convex sub-problems of the BS beamformer, and the D-STAR amplitude as well as phase shift configurations. We also propose a D-STAR based active beamforming and passive STAR-RIS amplitude/phase (DBAP) optimization scheme to solve the respective sub-problems by Lagrange dual with Dinkelbach’s transformation, alternating direction method of multipliers (ADMM) with successive convex approximation (SCA), and penalty convex-concave procedure (PCCP). Our simulation results reveal that the proposed D-STAR architecture outperforms the conventional single RIS, single STAR-RIS, and half-duplex networks. The proposed DBAP of D-STAR outperforms the state-of-the-art solutions found in the open literature for different numbers of quantization levels, geographic deployment, transmit power and for diverse numbers of transmit antennas, patch partitions as well as D-STAR elements.

Scalable algorithms are conceived for obtaining the sum-rate capacity of the reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input multiple-output (MIMO) broadcast channel (BC), where a multi-antenna base station (BS) transmits signals to multi-antenna users with the help of an RIS equipped with a massive number of finite-resolution programmable reflecting elements (PREs). As a byproduct, scalable path-following algorithms emerge for determining the sum-rate capacity of the conventional MIMO BCs, closing a long-standing open problem of information theory. The paper also develops scalable algorithms for maximizing the minimum rate (max-min rate optimization) of the users achieved by the joint design of RIS’s PRE and transmit beamforming for such an RIS-aided BC. The simulations provided confirm the high performance achieved by the algorithms developed, despite their low computational complexity.

A novel Physical Layer Security (PLS) framework is conceived for enhancing the security of wireless communication systems by exploiting multi-domain polarization in Multiple-Input Multiple-Output (MIMO) systems. We design a sophisticated key generation scheme based on multi-domain polarization and the corresponding receivers. An in-depth analysis of the system’s secrecy rate is provided, demonstrating the confidentiality of our approach in the presence of eavesdroppers having strong computational capabilities. More explicitly, our simulation results and theoretical analysis corroborate the advantages of the proposed scheme in terms of its bit error rate (BER), block error rate (BLER), and maximum achievable secrecy rate. Our findings indicate that the innovative PLS framework effectively enhances the security and reliability of wireless communication systems. For example, in a$4\times 4$MIMO setup, the proposed PLS strategy exhibits an improvement of 2dB compared to conventional MIMO, systems at a BLER of$2\cdot 10^{-5}$while the eavesdropper’s BLER reaches 1.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions.

In this work, we investigate the performance of reconfigurable intelligent surface (RIS) assisted near-field wideband system. By considering the large-scale effect of a high-dimensional RIS and frequency-selective channels, we derive an accurate array manifold of the RIS in the near-field from the scattering point of view. Subsequently, we conceive a near-optimal RIS phase design for a single-user scenario to alleviate the beam-squint effect of the wideband system. As for the multi-user case, we provide a virtual-subarray-based phase shift design, which mitigates the beam-squint effect as well as the mitigates deleterious effects of beam concentration. Numerical results show that the achievable data rate can be significantly improved by the proposed schemes compared to the benchmarks both in the single-user and multi-user cases. Explicitly, in the multi-user system, by leveraging the virtual-subarray-based phase design, the achievable sum-rate can be doubled compared to the conventional benchmarks.

We propose a hybrid beamforming architecture for near-field reconfigurable holographic surfaces (RHS) harnessed in cell-free networks. Specifically, the holographic beamformer of each base station (BS) is designed for maximizing the channel gain based on the local channel state information (CSI). By contrast, the digital beamformer at the central processing unit is designed based on the minimum mean squared error criterion. Furthermore, the near-field spectral efficiency of the RHS in cell-free networks is derived theoretically by harnessing the popular stochastic geometry approach. We consider both the phase shift error (PSE) at the RHS elements and the hardware impairment (HWI) at the radio frequency (RF) chains of the transceivers. Furthermore, we theoretically derive the asymptotic capacity bound, when considering an infinite physical size for the RHS in the near-field channel model. The theoretical analysis and simulation results show that the PSE at the RHS elements and the HWI at the RF chains of transceivers limit the spectral efficiency in the high signal-to-noise ratio region. Moreover, we show that the PSE at the RHS elements and the HWI at the RF chains of BSs can be compensated by increasing the number of BSs. Finally, we also demonstrate that the ergodic spectral efficiency based on the near-field channel model is higher than that based on the far-field channel model assumption.

Semantic communication is increasingly viewed as a promising solution to improve the transmission efficiency. However, semantic communications are susceptible not only to physical channel impairments, but also to semantic impairments, which degrade semantic understanding at the receiver and disrupt the associated downstream tasks. Hence, we focus our attention on the robustness of semantic communications against semantic impairments. Specifically, we first categorize textual semantic impairments into three categories based on their sources. Then, we propose a robust deep learning enabled semantic communication system (R-DeepSC) by introducing a semantic corrector for robust semantic encoding so as to facilitate semantic transmission. Moreover, we develop a non-autoregressive version of R-DeepSC, namely NA-RDeepSC, which offers improved inference speed by relying on a non-autoregressive architecture and an adaptive generator embedded into the semantic decoder. NA-RDeepSC performs semantic decoding in parallel, hence reducing the decoding complexity fromO(n) toO(1) with a comparable performance to that of R-DeepSC. Our experimental results demonstrate the superior robustness of the proposed R-DeepSC and NA-RDeepSC architectures in eliminating semantic impairments, hence highlighting the significance of this work in advancing the development of robust semantic communications.

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.

Millimeter wave (mmWave) signals are sensitive to blockages in wireless channels. Traditional mmWave transceiver designs intend to harvest both beamsteering and spatial multiplexing gains, but without considering the potential change in the channel state incurred by sudden blockages. In this paper, we propose a blockage-resilient hybrid transceiver design for supporting robust data transmissions in the face of dynamic blockages. Upon exploiting the spatial structure of mmWave channels, we formulate a weighted spectral efficiency maximization problem by utilizing the statistical information concerning the potential future blockages of different path clusters, which uniquely distinguishes this work from existing transceiver optimization problems. On the basis of alternating optimization, we propose a two-stage algorithm to deal with the resultant non-convex problem riddled with highly coupled variables. First, we alternatively optimize the fully digital transmit precoder and receive equalizer by transforming the optimization problem into a quadratic form. Based on the Block Successive Upper-bound Minimization (BSUM) framework, the optimal fully digital precoder and equalizer can be found by exploiting the Karush-Kuhn-Tucker (KKT) conditions and the matrix monotonic method. Then, inspired by the sparse signal recovery philosophy, the hybrid analog/digital transceiver structure is designed for approximating the fully digital solution. Our numerical results show that the proposed design strikes an improved throughput vs. blockage-resilience trade-off compared to existing schemes, which demonstrates its superiority.

The recently-developed orthogonal time frequency space (OTFS) modulation is capable of transforming the time-varying fading of the time-frequency (TF) domain into the time-invariant fading representations of the delay-Doppler (DD) domain. The OTFS system using orthogonal frequency-division multiplexing (OFDM) as inner core naturally requires the subcarrier spacing (SCS) Δfto be larger than the maximum Doppler frequency ϑmax, i.e. Δf> ϑmax, when perfect channel state information (CSI) knowledge is assumed. However, for the first time in literature, we explicitly demonstrate that the practical OFDM-based OTFS systems have to double their SCS in order to facilitate CSI estimation, requiring Δf′ = 2Δf> 2ϑmax. In order to mitigate this loss, we propose a novel noncoherent OTFS system, which is capable of operating at Δf> ϑmax. The major challenge in this context is the mitigation of the DD-domain interference without CSI. Against this background, we draw an analogy between the input-output model of OTFS and that of V-BLAST, where V-BLAST’s blind inter-antenna interference mitigation technique is invoked. Moreover, we propose to partition the DD-domain modulated symbols into groups, where space-time block coding is invoked in order to eliminate the DD-domain interference within each group. Our simulation results demonstrate that the proposed noncoherent OTFS is capable of substantially outperforming its coherent counterparts relying on CSI estimation.

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convex-solver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.

A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput.

Wireless surveillance, in which untrusted communications links are proactively monitored by legitimate agencies, has started to garner a lot of interest for enhancing the national security. In this paper, we propose a new cell-free massive multiple-input multiple-output (CF-mMIMO) wireless surveillance system, where a large number of distributed multi-antenna aided legitimate monitoring nodes (MNs) embark on either observing or jamming untrusted communication links. To facilitate concurrent observing and jamming, a subset of the MNs is selected for monitoring the untrusted transmitters (UTs), while the remaining MNs are selected for jamming the untrusted receivers (URs). We analyze the performance of CF-mMIMO wireless surveillance and derive a closed-form expression for the monitoring success probability of MNs. We then propose a greedy algorithm for the observing vs, jamming mode assignment of MNs, followed by the conception of a jamming transmit power allocation algorithm for maximizing the minimum monitoring success probability concerning all the UT and UR pairs based on the associated long-term channel state information knowledge. In conclusion, our proposed CF-mMIMO system is capable of significantly improving the performance of the MNs compared to that of the state-of-the-art baseline. In scenarios of a mediocre number of MNs, our proposed scheme provides an 11-fold improvement in the minimum monitoring success probability compared to its colocated mMIMO benchmarker.

The physical-layer security (PLS) of a space–ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely, the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely, the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and Dinkelbach’s method, which allow us to approximate nonconvex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the wireless propagation environment of the future Internet of Things (IoT). Recently, they have also been configured as a transmitter to realize information modulation at low hardware complexity. In this article, we conceive a transmitter relying on a single radio frequency (RF) chain for low-complexity RIS-aided multiuser downlink communication. More explicitly, in the proposed architecture, the multiuser information is transmit precoded and modulated at the RIS by appropriately configuring the phase shift and amplitude of each RIS element. We assume that the distribution of multiple users obeys on a Poisson point process (PPP), where we jointly optimize the total power reflected from the RIS and the power allocation fraction assigned to each user, under the practical constraint of a realistic amplitude limitation of each RIS element. Additionally, we theoretically analyze the ergodic rate, symbol error probability, outage probability, and coverage range of the proposed RIS-aided single-RF downlink and confirm the accuracy of our analysis by simulations. Finally, we compare its performance to that of the conventional multiple-input-multiple-output (MIMO) systems employing multiple RF-chains.

To mitigate the overlapping of the radar and communication frequency bands caused by large-scale devices access, we propose a novel integrated sensing and communication (ISAC) system, where a micro base station (MiBS) simultaneously carries out both target sensing and cooperative communication. Concretely, the MiBS, acting as the sensing equipment, can also serve as a full-duplex decode-and-forward relay to assist end-to-end communication. Moreover, nonorthogonal downlink transmission (NO-DLT) is adopted between the macro base station and the Internet of Things devices, so that the spectrum utilization can be further improved. To facilitate the performance evaluation, both the exact and asymptotic outage probabilities, the ergodic rates associated communication, and the probability of successful sensing detection are characterized. Subsequently, a pair of problems of maximizing the receive signal-to-interference-plus-noise ratio of the sensing signal and maximizing the sum rate of communication are formulated that are solved by the classic Lagrangian method while exploiting the associated function monotonicity. Our simulation results demonstrate that: 1) The proposed ISAC NO-DLT system improves both the communication and sensing performance under the same power consumption as noncooperative NO-DLT and 2) the proposed power allocation (PA) schemes are superior to the random PA scheme.

Hybrid transceivers are designed for linear decentralized estimation (LDE) in a mmWave multiple-input–multiple-output (MIMO) IoT network (IoTNe). For a noiseless fusion center (FC), it is demonstrated that the mean squared error (MSE) performance is determined by the number of RF chains used at each IoT node (IoTNo). Next, the minimum-MSE RF transmit precoders (TPCs) and receiver combiner (RC) matrices are designed for this setup using the dominant array response vectors, and subsequently, a closed-form expression is obtained for the baseband (BB) TPC at each IoTNo using Cauchy’s interlacing theorem. For a realistic noisy FC, it is shown that the resultant MSE minimization problem is nonconvex. To address this challenge, a block-coordinate descent-based iterative scheme is proposed to obtain the fully digital TPC and RC matrices followed by the simultaneous orthogonal matching pursuit (SOMP) technique for decomposing the fully digital transceiver into its corresponding RF and BB components. A theoretical proof of the convergence is also presented for the proposed iterative design procedure. Furthermore, robust hybrid transceiver designs are also derived for a practical scenario in the face of channel state information (CSI) uncertainty. The centralized MMSE lower bound has also been derived that benchmarks the performance of the proposed LDE schemes. Finally, our numerical results characterize the performance of the proposed transceivers as well as corroborate our various analytical propositions.

Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter-wave (mmWave) multiple-input–multiple-output (MIMO) Internet of Things Network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoT node power constraints, while also eliminating the need for a baseband receiver combiner at the fusion center (FC). To circumvent the nonconvexity of the hybrid beamformer design problem, the proposed approach initially determines the minimum mean-square error (MMSE) digital transmit precoder (TPC) weights followed by a simultaneous orthogonal matching pursuit (SOMP)-based framework for obtaining the analog RF and digital baseband TPCs. Robust hybrid beamformers are also derived for the realistic imperfect channel state information (CSI) scenario, utilizing both the stochastic and norm ball CSI uncertainty frameworks. The centralized MMSE bound derived in this work serves as a lower bound for the estimation performance of the proposed hybrid TPC designs. Finally, our simulation results quantify the benefits of the various designs developed.

Robust linear decentralized tracking of a time-varying sparse parameter is studied in a multiple-input–multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order to track the time-varying sparse parameter. Subsequently, an optimization problem is formulated to minimize the mean-square error (MSE) in each time slot (TS), followed by the design of a fast block coordinate descent (FBCD)-based iterative algorithm. A unique aspect of the proposed technique is that it requires only a single iteration per TS to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion. The recursive Bayesian Cramer–Rao bound (BCRB) is also derived for benchmarking the performance of the proposed linear decentralized estimation (LDE) scheme. Furthermore, for considering a practical scenario having CSI uncertainty, a robust SBL-KF (RSBL-KF) is derived for tracking the unknown parameter vector of interest followed by the conception of a robust transceiver design. Our simulation results show that the schemes designed outperform both the traditional sparsity-agnostic Kalman filter and the state-of-the-art sparse reconstruction methods. Furthermore, as compared to the uncertainty-agnostic design, the robust transceiver architecture conceived is shown to provide improved estimation performance, making it eminently suitable for practical applications.

The recently developed reconfigurable intelligent surfaces (RISs) are capable of improving the coverage of space–air–ground integrated networks (SAGINs), where the signals can be reflected in the desired direction without relying on power-thirsty radio-frequency (RF) chains. However, in the face of the substantially increased Doppler frequency, the classic orthogonal frequency-division multiplexing (OFDM) becomes inadequate in supporting RIS for the following reasons. First, the detrimental doubly selective fading leads to intersymbol interference (ISI) and intercarrier interference (ICI), which result in error floors for OFDM operating in the time–frequency (TF) domain. Second, it is far from trivial to configure RIS based on the time-varying fading channels. Third, the interpolation-based TF-domain channel estimation methods become impractical for the high-Doppler and high-dimensional RIS systems. Against this background, in this article, we propose the powerful 2-D orthogonal time–frequency space (OTFS) modulation for RIS-aided SAGINs, which transforms the time-varying fading encountered in the TF-domain to the time-invariant fading in the delay-Doppler (DD) domain. More explicitly, first, for the first time in the literature, we devise the DD-domain channel model of RIS-assisted SAGINs in the face of doubly selective fading. Second, in order to facilitate the RIS configuration in the DD-domain, we propose to create “virtual” Doppler frequencies that guide the phase changes at the RIS, even though the RIS phase rotations do not suffer from Doppler effects. Third, we conceive an attractive DD-domain RIS channel estimation method that can support both OFDM and OTFS, where the TF-domain interpolation is eliminated. Our simulation results demonstrate that the proposed DD-domain RIS configuration and channel estimation methods for both OFDM and OTFS are capable of mitigating the error floors encountered in the TF-domain. Furthermore, our simulation results confirm that OTFS-based RIS-assisted SAGIN systems are capable of outperforming their OFDM counterparts and exhibit excellent performance across a wide range of SAGIN channel parameters including the Ricean K factor, Doppler frequency, delay spread, coverage distance, and carrier frequency.

As a promising candidate for ultrareliable and low-latency communications, the recent permutation-based transmission concept substantially improves the resource utilization efficiency in the Internet of Things (IoT). In this context, the Age of Information (AoI) experienced in permutation-based short-packet transmissions is characterized in a wiretap channel, where eavesdroppers are wiretapping the status updates delivered over the legitimate link. The AoI of the legitimate link and the secrecy margin of the wiretap channel are formulated in closed forms within the regime of finite-blocklength information theory to quantify the data freshness and security of status updates in the IoT. The optimal packet structure to be delivered over the network interface is found by solving the optimization problems of minimizing the legitimate link’s AoI and maximizing the secrecy margin. Illustrative numerical results are provided for our permutation-based transmission to quantify its performance gains over the conventional encapsulation, specifically in short-packet communications.

Indoor localization is of pivotal significance for a wide variety of services in the context of the Internet of Things (IoT). Both ranging-based and fingerprint-based localization techniques are promising for employment in harsh indoor environments. Hence, we propose a unified framework based on factor graphs for ubiquitous high-accuracy indoor localization. Our unified framework efficiently integrates ranging and fingerprinting for striking an appealing accuracy versus deployment cost tradeoff, where the crowdsourcing required for the construction of fingerprinting databases can also be addressed with little human intervention. By intrinsically amalgamating the global grid sampling and the regularized importance-resampling techniques, a nonparametric belief propagation algorithm is proposed for achieving the accurate position estimation at the cost of a moderate computational complexity. For improving the robustness to environmental variations, a likelihood-ratio-based approach is employed to detect ranging outliers. Moreover, a low-complexity serial scheduling scheme defined over factor graphs is designed for real-time localization. We design a hybrid ultrawide bandwidth and Wi-Fi localization system relying on off-the-shelf commercial devices and evaluate the proposed unified framework in a typical office building. Our experimental results show that the proposed algorithm outperforms the existing state-of-the-art methods and it is capable of achieving submeter localization accuracy.

A revolutionary technology relying on Stacked Intelligent Metasurfaces (SIM) is capable of carrying out advanced signal processing directly in the native electromagnetic (EM) wave regime. An SIM is fabricated by a sophisticated amalgam of multiple stacked metasurface layers, which may outperform its single-layer metasurface counterparts, such as reconfigurable intelligent surfaces (RIS) and metasurface lenses. We harness this new SIM for implementing holographic multiple-input multiple-output (HMIMO) communications without requiring excessive radio-frequency (RF) chains, which is a substantial benefit compared to existing implementations. First of all, we propose an HMIMO communication system based on a pair of SIM at the transmitter (TX) and receiver (RX), respectively. In sharp contrast to the conventional MIMO designs, SIM is capable of automatically accomplishing transmit precoding and receiver combining, as the EM waves propagate through them. As such, each spatial stream can be directly radiated and recovered from the corresponding transmit and receive port. Secondly, we formulate the problem of minimizing the error between the actual end-to-end channel matrix and the target diagonal one, representing a flawless interference-free system of parallel subchannels. This is achieved by jointly optimizing the phase shifts associated with all the metasurface layers of both the TX-SIM and RX-SIM. We then design a gradient descent algorithm to solve the resultant non-convex problem. Furthermore, we theoretically analyze the HMIMO channel capacity bound and provide some fundamental insights. Finally, extensive simulation results are provided for characterizing our SIM-aided HMIMO system, which quantifies its substantial performance benefits, e.g., 150% capacity improvement over both conventional MIMO and its RIS-aided counterparts.

Sixth-Generation (6G) technologies will revolutionize the wireless ecosystem by enabling the delivery of futuristic services through satellite-terrestrial integrated networks (STINs). As the number of subscribers connected to STINs increases, it becomes necessary to investigate whether the edge computing paradigm may be applied to low Earth orbit satellite (LEOS) networks for supporting computation-intensive and delay-sensitive services for anyone, anywhere, and at any time. Inspired by this research dilemma, we investigate a LEOS edge-assisted multi-layer multi-access edge computing (MEC) system. In this system, the MEC philosophy will be extended to LEOS, for defining the LEOS edge, in order to enhance the coverage of the multi-layer MEC system and address the users’ computing problems both in congested and isolated areas. We then design its operating offloading framework and explore its feasible implementation methodologies. In this context, we formulate a joint optimization problem for the associated communication and computation resource allocation for minimizing the overall energy dissipation of our LEOS edge-assisted multi-layer MEC system while maintaining a low computing latency. To solve the optimization problem effectively, we adopt the classic alternating optimization (AO) method for decomposing the original problem and then solve each sub-problem using low-complexity iterative algorithms. Finally, our numerical results show that the offloading scheme conceived achieves low computing latency and energy dissipation compared to the state-of-the-art solutions, a single layer MEC supported by LEOS or base stations (BS).

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices’ wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme.

Resource allocation is conceived for cell-free (CF) massive multi-input multi-output (MIMO)-aided ultra-reliable and low latency communication (URLLC) systems. Specifically, to support multiple devices with limited pilot overhead, pilot reuse among the users is considered, where we formulate a joint pilot length and pilot allocation strategy for maximizing the number of devices admitted. Then, the pilot power and transmit power are jointly optimized while simultaneously satisfying the devices’ decoding error probability, latency, and data rate requirements. Firstly, we derive the lower bounds (LBs) of ergodic data rate under finite channel blocklength (FCBL). Then, we propose a novel pilot assignment algorithm for maximizing the number of devices admitted. Based on the pilot allocation pattern advocated, the weighted sum rate (WSR) is maximized by jointly optimizing the pilot power and payload power. To tackle the resultant NP-hard problem, the original optimization problem is first simplified by sophisticated mathematical transformations, and then approximations are found for transforming the original problems into a series of subproblems in geometric programming (GP) forms that can be readily solved. Simulation results demonstrate that the proposed pilot allocation strategy is capable of significantly increasing the number of admitted devices and the proposed power allocation achieves substantial WSR performance gain.

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms both the popular Multi-User Linear Precoding (MU–LP) and the conventional Non-Orthogonal Multiple Access (NOMA) system. Finally, the BCD framework is shown to generate near-optimal solutions at reduced complexity.

Photon counting is an effective detection technique for weak optical signals in underwater optical wireless communications (UOWC). This paper proposes a new approach for power allocation in an uplink$M$-ary pulse position modulation (PPM), photo-counting non-orthogonal multiple-access (PhC-NOMA) system. Different from existing techniques in photon-counting systems, the new approach supports consistent duty cycles across underwater devices and adjusts the transmit rates of the devices through their transmit powers, thereby avoiding the delays of duty cycle adjustments and supporting high-speed transmissions. Power allocation is non-trivial in photon-counting systems due to signal-dependent Poisson shot noises. As a key contribution, we derive the exact and asymptotic expressions for the achievable rate of the$M$-ary PPM PhC-NOMA system with the signal-dependent Poisson shot noise and multiuser interference considered. With the expressions, we reveal the received power at the base station (BS) is minimized when their minimum data rate requirements are delivered and can be solved using an incremental algorithm. We also asymptotically maximize the photon efficiency of the devices while preventing the saturation of the receiving photon detector, using Karush-Kuhn-Tucker (KKT) conditions. Simulations show that our approach can reduce the received power at the BS by up to 25% and double the photon efficiency, as compared to the existing techniques.

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network’s spectral efficiency.As for our analytical contributions, upon coherently combing the signals received by the central processing unit (CPU) from the users through the space and terrestrial links, we first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques employed at the APs and the satellite under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput, when maximum ratio combining is utilized for detecting the desired signals.As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed allow us to address the congestion issues appearing when at least one user is served at a rate below his/her target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

Space-ground integrated networks (SGINs) have attracted substantial research interests due to their wide area coverage capability, where spectrum sharing is employed between the satellite and terrestrial networks for improving the spectral efficiency (SE). We further improve the SE by conceiving a cell-free system in SGINs, where the full-duplex (FD) multi-antenna APs simultaneously provide downlink and uplink services at the same time and within the same frequency band. Furthermore, power domain (PD) non-orthogonal multiple access (NOMA) is employed as the multiple access (MA) technique in the cell-free system. To achieve a performance enhancement, the sum-rate maximization problem is formulated for jointly optimizing the power allocation factors (PAFs) of the NOMA downlink (DL), the uplink transmit power, and both the beamformer of the satellite and of the APs. Successive convex approximation (SCA) and semi-definite programming (SDP) are adopted to transform the resultant non-convex problem into an equivalent convex one. Our simulation results reveal that 1) our proposed system outperforms the well-known approaches (i.e., frequency division duplex (FDD) and small cell systems) in terms of its SE; 2) our proposed optimization algorithm significantly improves the networking performance; 3) the conceived SIC order design outperforms the fixed-order design at the same complexity.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

Metaverse aims for building a fully immersive virtual shared space, where the users are able to engage in various activities. To successfully deploy the service for each user, the Metaverse service provider and network service provider generally localise the user first and then support the communication between the base station (BS) and the user. A reconfigurable intelligent surface (RIS) is capable of creating a reflected link between the BS and the user to enhance line-of-sight. Furthermore, the new key performance indicators (KPIs) in Metaverse, such as its energy-consumption-dependent total service cost and transmission latency, are often overlooked in ultra-reliable low latency communication (URLLC) designs, which have to be carefully considered in next-generation URLLC (xURLLC) regimes. In this paper, our design objective is to jointly optimise the transmit power, the RIS phase shifts, and the decoding error probability to simultaneously minimise the total service cost and transmission latency and approach the Pareto Front (PF). We conceive a twin-stage central controller, which aims for localising the users first and then supports the communication between the BS and users. In the first stage, we localise the Metaverse users, where the stochastic gradient descent (SGD) algorithm is invoked for accurate user localisation. In the second stage, a meta-learning-based position-dependent multi-objective soft actor and critic (MO-SAC) algorithm is proposed to approach the PF between the total service cost and transmission latency and to further optimise the latency-dependent reliability. Our numerical results demonstrate that 1) The proposed solution strikes a tradeoff between the total service cost and transmission latency, which provides a candidate group of optimal solutions for diverse practical scenarios. 2) The proposed meta-learning-based MO-SAC algorithm is capable of adaption to new wireless environments, compared to the benchmarkers. 3) The approximate PF depicted discovered the relationships among the KPIs for the Metaverse, which provides guidelines for its deployment.

Reconfigurable intelligent surfaces (RIS) are capable of beneficially ameliorating the propagation environment by appropriately controlling the passive reflecting elements. To extend the coverage area, the concept of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has been proposed, yielding supporting 360° coverage user equipment (UE) located on both sides of the RIS. In this paper, we theoretically formulate the ergodic sum-rate of the STAR-RIS assisted non-orthogonal multiple access (NOMA) uplink in the face of channel estimation errors and hardware impairments (HWI). Specifically, the STAR-RIS phase shift is configured based on the statistical channel state information (CSI), followed by linear minimum mean square error (LMMSE) channel estimation of the equivalent channel spanning from the UEs to the access point (AP). Afterwards, successive interference cancellation (SIC) is employed at the AP using the estimated instantaneous CSI, and we derive the theoretical ergodic sum-rate upper bound for both perfect and imperfect SIC decoding algorithm. The theoretical analysis and the simulation results show that both the channel estimation and the ergodic sum-rate have performance floor at high transmit power region caused by transceiver hardware impairments.

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.

Space-time shift keying-aided orthogonal time frequency space modulation-based multiple access (STSK-OTFS-MA) is proposed for reliable uplink transmission in high-Doppler scenarios. As a beneficial feature of our STSK-OTFS-MA system, extra information bits are mapped onto the indices of the active dispersion matrices, which allows the system to enjoy the joint benefits of both STSK and OTFS signalling. Due to the fact that both the time-, space- and DD-domain degrees of freedom are jointly exploited, our STSK-OTFS-MA achieves increased diversity and coding gains. To mitigate the potentially excessive detection complexity, the sparse structure of the equivalent transmitted symbol vector is exploited, resulting in a pair of low-complexity near-maximum likelihood (ML) multiuser detection algorithms. Explicitly, we conceive a progressive residual check-based greedy detector (PRCGD) and an iterative reduced-space check-based detector (IRCD). Then, we derive both the unconditional single-user pairwise error probability (SU-UPEP) and a tight bit error ratio (BER) union-bound for our single-user STSK-OTFS-MA system employing the ML detector. Furthermore, the discrete-input continuous-output memoryless channel (DCMC) capacity of the proposed system is derived. The optimal dispersion matrices (DMs) are designed based on the maximum attainable diversity and coding gain metrics. Finally, it is demonstrated that our STSK-OTFS-MA system achieves both a lower BER and a higher DCMC capacity than its conventional spatial modulation (SM) and its orthogonal frequency-division multiplexing (OFDM) counterparts. As a benefit, the proposed system strikes a compelling BER vs. system complexity as well as BER vs. detection complexity trade-offs.

Conventional centralized authentication techniques based on both digital cryptography and physical-layer attributes are prone to single-point failure due to either compromised digital security keys or an abrupt change in the physical communication environment. Although these particular challenges could be mitigated by the joint use of decentralized authentication and physical-layer attributes, such schemes often exhibit unpredictable performance. Simultaneously, the necessary involvement of multiple parties and the imperfect observation of the physical communication environment can also significantly increase the latency and computational complexity. As a remedy, a decentralized authentication scheme is proposed in this paper to achieveGuaranteed Authentication Level at Minimized Complexity(GALAMC) based on the intelligent use of distributed collaboration and available distributive physical-layer attributes. Specifically, we aim for minimizing the complexity of the proposed collaborative authentication process by harnessing the minimum number of collaborative nodes and the selected authentication attributes at each node across the different environments while guaranteeing the required authentication level. The related physical-layer authentication scheme is implemented at each collaborative node where different physical-layer attributes can be selected based on their usefulness which is time-varying. The simulation results demonstrate that our scheme maintains the target level of authentication and it is more immune to sudden environmental changes than the conventional centralized physical-layer authentication scheme. It can also be observed that our proposed scheme can adaptively select the minimum number of collaborative nodes for adaptively minimizing the computational cost.

In this paper, rapidly converging low-complexity iterative transmit precoding (TPC) techniques are proposed for the massive multiple-input multiple-output (MIMO) downlink. First of all, the proposed random block-based iterative TPC (RBI-TPC) algorithm performs its iterations by updating multiple rather than a single component at each instant, where the updating order of each block containing multiple components relies on the samples randomly sampled from a discrete distribution. Based on the analytically derived convergence rate, we demonstrate that improved convergence is achieved by the block-based update mechanism conceived since the correlation between multiple components can be beneficially exploited. Then, the random sampling that determines the updating order is studied. By applying conditional random sampling, the updating order is optimized based on the latest updates for attaining more rapid convergence. We also demonstrate that the associated updating order may become deterministic under specific conditions so that a fixed but optimized updating order can be used for facilitating the practical implementations, which paves the way for conceiving the ordered block-based iterative TPC (OBI-TPC) algorithm. Finally, the concept of successive over-relaxation (SOR) is adopted for further convergence improvement and simulations are presented to illustrate the performance improvements of the proposed RBI and OBI TPC algorithms compared to the existing low-complexity iterative TPC schemes.

Beamspace millimeter-wave (mmWave) and terahertz (THz) massive MIMO constitute attractive schemes for next-generation communications, given their abundant bandwidth and high throughput. However, their user and beam selection problem has not been efficiently addressed yet. Inspired by this challenge, we develop low-complexity solutions explicitly. In contrast to the zero forcing in the prior art, we introduce the dirty paper coding (DPC) into the joint user and beam selection problem. We unveil the compelling properties of the DPC sum rate in beamspace massive MIMO, showing its monotonic evolution against the number of users and beams selected. We then exploit its beneficial properties for substantially simplifying the joint user and beam selection problem. Furthermore, we develop a set of algorithms striking unique trade-offs for solving the simplified problem, facilitating simultaneous user and beam selection based on partial beamspace channels for the first time. Additionally, we derive the sum rate bound of the algorithms and analyze their complexity. Our simulation results validate the effectiveness of the proposed design and analysis, confirming their superiority over prior solutions.

In many core problems of signal processing and wireless communications, Karush-Kuhn-Tucker (KKT) conditions based optimization plays a fundamental role. Hence we investigate the KKT conditions in the context of optimizing positive semidefinite matrix variables under nonconvex rank constraints. More explicitly, based on the properties of KKT conditions, we optimize a reconfigurable intelligent surface (RIS) aided multi-user multi-input multi-output (MU-MIMO) network. Specifically, we consider the capacity maximization and sum mean square error (MSE) minimization problems of both the RIS-aided MU-MIMO uplink (UL) and downlink (DL) under multiple weighted power constraints and rank constraints. As for the RIS-aided MU-MIMO UL, the optimal structures of the signal covariance matrices are derived based on the KKT conditions. Furthermore, an efficient procedure is designed for solving the capacity maximization and sum mean square error (MSE) minimization problems. Then the UL-DL dualities are exploited for solving the capacity maximization and MSE minimization problems of the RIS-aided MU-MIMO DL based on the results of the UL optimization. Hence in the proposed framework, the phase shifting matrix of the RIS is jointly optimized with the signal covariance matrices for both the UL and DL. Our simulation results demonstrate the performance advantages of the proposed framework.

The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs). However, the conventional RISs require that the WDs have to be located in the half-plane of the reflection space, which restricts their potential benefits. To address this issue, the novel family of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is considered in AirComp systems to improve the computation accuracy across a wide coverage area. To minimize the computation mean-squared-error (MSE) in STAR-RIS assisted AirComp systems, we propose a joint beamforming design for optimizing both the transmit power at the WDs, as well as the passive reflect and transmit beamforming matrices at the STAR-RIS, and the receive beamforming vector at the fusion center (FC). Specifically, in the updates of the passive reflect and transmit beamforming matrices, closed-form solutions are derived by introducing an auxiliary variable and exploiting the coupled binary phase-shift conditions. Moreover, by assuming that the number of antennas at the FC and that of elements at the STAR-RIS/RIS are sufficiently high, we theoretically prove that the STAR-RIS assisted AirComp systems provide higher computation accuracy than the conventional RIS assisted systems. Our numerical results show that the proposed beamforming design outperforms the benchmark schemes relying on random phase-shift constraints and the deployment of conventional RIS. Moreover, its performance is close to the lower bound achieved by the beamforming design based on the STAR-RIS dispensing with coupled phase-shift constraints.

The new concept of semi-integrated-sensing-and-communication (Semi-ISaC) is proposed for next-generation cellular networks. Compared to the state-of-the-art, where the total bandwidth is used for integrated sensing and communication (ISaC), the proposed Semi-ISaC framework provides more freedom as it allows that a portion of the bandwidth is exclusively used for either wireless communication or radar detection, while the rest is for ISaC transmission. To enhance the bandwidth efficiency (BE), we investigate the evolution of Semi-ISaC networks from orthogonal multiple access (OMA) to non-orthogonal multiple access (NOMA). First, we evaluate the performance of an OMA-based Semi-ISaC network. As for the communication signals, we investigate both the outage probability (OP) and the ergodic rate. As for the radar echoes, we characterize the ergodic radar estimation information rate (REIR). Then, we investigate the performance of a NOMA-based Semi-ISaC network, including the OP and the ergodic rate for communication signals and the ergodic REIR for radar echoes. The diversity gains of OP and the high signal-to-noise ratio (SNR) slopes of the ergodic REIR are also evaluated as insights. The analytical results indicate that: 1) Under a two-user NOMA-based Semi-ISaC scenario, the diversity order of the near-user is equal to the coefficient of the Nakagami-${m}$fading channels ($m$), while that of the far-user is zero; and 2) The high-SNR slope for the ergodic REIR is based on the ratio of the radar signal’s duty cycle to the pulse duration. Our simulation results show that: 1) Semi-ISaC has better channel capacity than the conventional ISaC; and 2) The NOMA-based Semi-ISaC has better channel capacity than the OMA-based Semi-ISaC.

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

Anintelligent reflecting surface (IRS)-aided wireless-powered mobile edge computing (WP-MEC) system is conceived, where each device’s computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. To fully unleash the potential benefits of the IRS, employing multiple IRS beamforming (BF) patterns/vectors in the considered operating frame to create time-selectivity channels, i.e., dynamic IRS BF (DIBF), is in principle possible at the cost of additional signaling overhead. To strike a balance between the system performance and associated signalling overhead, we propose three cases of DIBF configurations based on the maximum number of IRS reconfiguration times. The degree-of-freedom provided by the IRS may introduce different impacts on the TDMA and NOMA-based UL offloading schemes. Thus, it is still fundamentally unknown which multiple access scheme is superior for MEC UL offloading by considering the impact of the IRS. To answer this question, we provide a comprehensively theoretical performance comparison for the TDMA and NOMA-based offloading schemes under the three cases of DIBF configurations by characterizing their achievable computation rate. Analytical results demonstrate that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, computation offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Then, we propose computationally efficient algorithms by invoking alternating optimization for solving their associated computation rate maximization problems. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes and also unveil that the optimal time allocated to downlink wireless power transfer can be effectively reduced with the aid of IRSs, which is beneficial for both the system’s spectral efficiency and its energy efficiency.

Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-of-sight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/ full-RF and multiplexing-/diversity-oriented setups.

This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.

A recently proposed unified precoding and pilot design optimization (UPPiDO) framework offers a reduction in both training and feedback overhead of acquiring channel state information (CSI) and an enhancement in robustness (to CSI uncertainties) at the expense of a more computationally demanding precoding optimization. To address this increased complexity, in this paper we first propose an unfolding-friendly iterative algorithm, which can efficiently address a family of non-convex and non-smooth problems. Then, we develop an efficient approach to unfold the iterative algorithm designed. Besides being applicable to important and typical iterative optimization algorithms, a pivotal advantage of the proposed unfolding approach is that the trainable parameters are scalars (rather than matrices). This, in turn, reduces the number of training samples required and makes it suitable for rapidly fluctuating wireless environments. We apply the algorithm unfolding (AU) techniques developed to our UPPiDO-based symbol-level precoding and block-level precoding. Our complexity analysis indicates that the computational complexity is scalable both with the numbers of served users and antennas. Our simulation results demonstrate that the number of outer iterations (or layers) required is about 1/3 of that of the original iterative algorithms.

Reconfigurable intelligent surfaces (RISs) achieve high passive beamforming gains for signal enhancement or interference nulling by dynamically adjusting their reflection coefficients. Their employment is particularly appealing for improving both the wireless security and the efficiency of radio frequency (RF)-based wireless power transfer. Motivated by this, we conceive and investigate a RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system designed for information and power transfer from a base station (BS) to an information user (IU) and to multiple energy users (EUs), respectively. Moreover, the EUs are also potential eavesdroppers that may overhear the communication between the BS and IU. We adopttwo-timescaletransmission for reducing the signal processing complexity as well as channel training overhead, and aim for maximizing the average worst-case secrecy rate achieved by the IU. This is achieved by jointly optimizing theshort-termtransmit beamforming vectors at the BS (including information and energy beams) as well as thelong-termphase shifts at the RIS, under the energy harvesting constraints considered at the EUs and the power constraint at the BS. The stochastic optimization problem formulated is non-convex with intricately coupled variables, and is non-smooth due to the existence of multiple EUs/eavesdroppers. No standard optimization approach is available for this challenging scenario. To tackle this challenge, we propose a smooth approximation aided stochastic successive convex approximation (SA-SSCA) algorithm. Furthermore, a low-complexity heuristic algorithm is proposed for reducing the computational complexity without unduly eroding the performance. Simulation results show the efficiency of the RIS in securing SWIPT systems. The significant performance gains achieved by our proposed algorithms over the relevant benchmark schemes are also demonstrated.

This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite. By coherently combing the signals received by the central processing unit from the users through the space and terrestrial links, we mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. A closed-form expression is obtained when maximum ratio combining is employed to detect the desired signals. We formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics to perform power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated based on a closed-form expression. The mathematical analysis is validated with numerical results showing the added benefits of considering a satellite link in terms of improving the ergodic data throughput.

Unmanned aerial vehicles (UAVs) are capable of enhancing the coverage of existing cellular networks by acting as aerial base stations (ABSs). Due to the limited on-board battery capacity and dynamic topology of UAV networks, trajectory planning and interference coordination are crucial for providing satisfactory service, especially in emergency scenarios, where it is unrealistic to control all UAVs in a centralized manner by gathering global user information. Hence, we solve the decentralized joint trajectory and transmit power control problem of multi-UAV ABS networks. Our goal is to maximize the number of satisfied users, while minimizing the overall energy consumption of UAVs. To allow each UAV to adjust its position and transmit power solely based on local- rather the global-observations, a multi-agent reinforcement learning (MARL) framework is conceived. In order to overcome the non-stationarity issue of MARL and to endow the UAVs with distributed decision making capability, we resort to the centralized training in conjunction with decentralized execution paradigm. By judiciously designing the reward, we propose a decentralized joint trajectory and power control (DTPC) algorithm with significantly reduced complexity. Our simulation results show that the proposed DTPC algorithm outperforms the state-of-the-art deep reinforcement learning based methods, despite its low complexity.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric user (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. A beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed scheme over the benchmark schemes.

Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and low power consumption. In this article, we propose an RIS-assisted mmWave point-to-point system relying on dynamically configured subarray connected hybrid beamforming structures. More explicitly, an energy-efficient analog beamformer relying on the twin-resolution phase shifters is proposed. Then, we conceive a successive interference cancelation (SIC)-based method for jointly designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized for maximizing each subarray’s rate. Furthermore, a greedy method is proposed for determining the phase shifter configuration of each subarray. We then propose to update the RIS elements relying on a complex circle manifold (CCM)-based method. The proposed dynamic subconnected structure as well as the proposed joint hybrid and passive beamforming method strike an attractive tradeoff between the bandwidth efficiency and power consumption. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.

Data packet routing in aeronauticalad hocnetworks (AANETs) is challenging due to their high-dynamic topology. In this article, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multiobjective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity, as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

Over-the-air computation (AirComp) has been recognized as a promising technique of enabling the fusion center (FC) to aggregate the data gleaned from massive distributed wireless devices (WDs). Nevertheless, the computational performance of AirComp is significantly affected by the potentially poor channel conditions between the WDs and FC due to physical obstacles. For mitigating this limitation, we employ reconfigurable intelligent surfaces (RISs) for enhancing the reception quality and, thus, improve the computational performance of AirComp. Moreover, the previous studies of RIS-assisted AirComp tend to rely on the real-time channel state information (CSI), leading to excessive overhead since the number of RIS elements is large. To mitigate the above issue, a mixed-timescale penalty-dual-decomposition (MTPDD) algorithm is proposed, in which the transmit power of each WD, the receive beamforming vector at the FC, and the passive beamforming matrix of the RIS are jointly optimized. We aim to minimize the average computation mean-squared error (MSE) over time with reduced signaling overhead. Specifically, at each time slot, we optimize the short-term transmit power and receive the beamforming vector based on the real-time low-dimensional CSI vectors. In contrast, in each frame, we update the long-term passive RIS beamforming matrix based on the channel statistics. Besides, we analyzed both the convergence and the computational complexity of the proposed algorithms. Simulation results verify the benefits of our proposed MTPDD beamforming algorithm. It is also shown that the performance of the MTPDD algorithm approaches that achieved by the scheme using real-time perfect CSI with reduced signal overhead.

The envisioned wireless networks of the future entail the provisioning of massive numbers of connections, heterogeneous data traffic, ultra-high spectral efficiency, and low latency services. This vision is spurring research activities focused on defining a next generation multiple access (NGMA) protocol that can accommodate massive numbers of users in different resource blocks, thereby, achieving higher spectral efficiency and increased connectivity compared to conventional multiple access schemes. In this article, we present a multiple access scheme for NGMA in wireless communication systems assisted by multiple reconfigurable intelligent surfaces (RISs). In this regard, considering the practical scenario of static users operating together with mobile ones, we first study the interplay of the design of NGMA schemes and RIS phase configuration in terms of efficiency and complexity. Based on this, we then propose a multiple access framework for RIS-assisted communication systems, and we also design a medium access control (MAC) protocol incorporating RISs. In addition, we give a detailed performance analysis of the designed RIS-assisted MAC protocol. Our extensive simulation results demonstrate that the proposed MAC design outperforms the benchmarks in terms of system throughput and access fairness, and also reveal a trade-off relationship between the system throughput and fairness.

Given the proliferation of the massive machine type communication devices (MTCDs) in beyond 5G (B5G) wireless networks, energy harvesting (EH) aided next generation multiple access (NGMA) systems have drawn substantial attention in the context of energy-efficient data sensing and transmission. However, without adaptive time slot (TS) and power allocation schemes, NGMA systems relying on stochastic sampling instants might lead to tardy actions associated both with high age of information (AoI) as well as high power consumption. For mitigating the energy consumption, we exploit a pair of sleep-scheduling policies, namely the multiple vacation (MV) policy and start-up threshold (ST) policy, which are characterized in the context of three typical multiple access protocols, including time-division multiple access (TDMA), frequency-division multiple access (FDMA) and non-orthogonal multiple access (NOMA). Furthermore, we derive closed-form expressions for the MTCD system’s peak AoI, which are formulated as the optimization objective under the constraints of EH power, status update rate and stability conditions. An exact linear search based algorithm is proposed for finding the optimal solution by fixing the status update rate. As a design alternative, a low complexity concave-convex procedure (CCP) is also formulated for finding a near-optimal solution relying on the original problem’s transformation into a form represented by the difference of two convex problems. Our simulation results show that the proposed algorithms are beneficial in terms of yielding a lower peak AoI at a low power consumption in the context of the multiple access protocols considered.

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence ofrandomlink delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost$(1+\beta)$times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with$\beta $being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as O-OFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate thisdouble spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, abeamformer-basedNOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where acluster-basedNOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed schemes over the benchmark schemes employing conventional transmission strategies.

A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.

Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from spectral leakage, when the reciprocal of the record length is not an integer multiple of the unknown phase, which incurs an accuracy degradation. For the existing single-sample estimation scheme, window-based methods have been proposed for spectral leakage mitigation. As a further advance, we propose a dual-frequency estimator, which asymptotically approaches the Cramér-Rao bound, when multiple samples are available. Numerical results show that the proposed estimator outperforms the existing window-based methods, when the number of samples is sufficiently high.

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the capacity of wireless networks at a low cost. In practical RIS-assisted communication systems, the acquisition of channel state information (CSI) and RIS reflection optimization constitute a pair of challenges. In this paper, a low-complexity channel estimation and passive beamforming design is proposed.First of all, we conceive a low-complexity framework for maximizing the achievable rate of RIS-assisted multiple-input multiple-output (MIMO) systems having discrete phase shifts at each RIS element. In contrast to existing solutions, the proposed arrangement partitions the channel training stage into several phases, where the RIS reflection coefficients are pre-designed and the effective superposed channel is estimated instead of separately training the source-destination and source-RIS-destination links. Based on this, the active beamformer can be designed at low complexity and the RIS reflection optimization is performed by selecting that one from the pre-designed training set which maximizes the achievable rate.Secondly, we propose novel techniques for generating the training set of RIS reflection coefficients. The theoretical performance of the proposed scheme is analyzed and compared to the optimal RIS configuration.Finally, our simulation results demonstrate that the proposed framework is more competitive than its existing counterparts when relying on imperfect CSI, especially for rapidly time-varying channels having short channel coherence time.

To realize the Quantum Internet, quantum communications require pre-shared entanglement among quantum nodes. However, both the generation and the distribution of the maximally-entangled quantum states are inherently contaminated by quantum decoherence. Conventionally, the quantum decoherence is mitigated by performing the consecutive steps of quantum entanglement distillation followed by quantum teleportation. However, this conventional approach imposes a long delay. To circumvent this impediment, we propose a novel quantum communication scheme relying on realistic noisy pre-shared entanglement, which eliminates the sequential steps imposing delay in the standard approach. More precisely, our proposed scheme can be viewed as a direct quantum communication scheme capable of improving the quantum bit error ratio (QBER) of the logical qubits despite relying on realistic noisy pre-shared entanglement. Our performance analysis shows that the proposed scheme offers competitive QBER, yield, and goodput compared to the existing state-of-the-art quantum communication schemes, despite requiring fewer quantum gates.

Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithm (QAOA) constitutes a promising candidate for demonstrating tangible quantum advantages based on NISQ devices. In this paper, we consider the maximum likelihood (ML) detection problem of binary symbols transmitted over a multiple-input and multiple-output (MIMO) channel, where finding the optimal solution is exponentially hard using classical computers. Here, we apply the QAOA for the ML detection by encoding the problem of interest into a level-$p$QAOA circuit having$2p$variational parameters, which can be optimized by classical optimizers. This level-$p$QAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately in$p$consecutive rounds. More explicitly, we first encode the optimal solution of the ML detection problem into the ground state of a problem Hamiltonian. Using the quantum adiabatic evolution technique, we provide both analytical and numerical results for characterizing the evolution of the eigenvalues of the quantum system used for ML detection. Then, for level-1 QAOA circuits, we derive the analytical expressions of the expectation values of the QAOA and discuss the complexity of the QAOA based ML detector. Explicitly, we evaluate the computational complexity of the classical optimizer used and the storage requirement of simulating the QAOA. Finally, we evaluate the bit error rate (BER) of the QAOA based ML detector and compare it both to the classical ML detector and to the classical minimum mean squared error (MMSE) detector, demonstrating that the QAOA based ML detector is capable of approaching the performance of the classical ML detector.

Novel hybrid beamformer designs are conceived for a multi-user multi-cell (MUMC) mmWave system relying on base station (BS) coordination and total transmit power minimization subject to realistic signal-to-interference-plus-noise ratio (SINR) constraints at each mobile station (MS). Initially, a semidefinite relaxation (SDR)-based approach is developed for a centralized MUMC system to determine the fully digital beamformer having perfect CSI. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the fully-digital (FD) solution into its analog and digital components for constructing a hybrid transceiver. Next, an alternating direction method of multipliers (ADMM) based distributed hybrid beamformer is designed for the same system, which requires only local CSI and limited information exchange among the BSs, thus avoiding the excessive signalling overheads required by the centralized approach. Then we further extend both the centralized and the above distributed hybrid designs to construct robust beamformers that minimize the worst-case transmit power with imperfect CSI. Our robust beamforming techniques leverage the S-lemma, which is eminently suitable for the infinitely many constraints arising from the associated CSI uncertainty. Finally, our simulation results demonstrate the improved performance of the proposed centralized and distributed methods over the system having no coordination.

Large-scale antenna arrays employed by the base station (BS) constitute an essential next-generation communications technique. However, due to the constraints of size, cost, and power consumption, it is usually considered unrealistic to use a large-scale antenna array at the user side. Inspired by the emerging technique of reconfigurable intelligent surfaces (RIS), we firstly propose the concept of user-specific RIS (US-RIS) for facilitating the employment of a large-scale antenna array at the user side in a cost- and energy-efficient way. In contrast to the existing employments of RIS, which belong to the family of base-station-specific RISs (BSS-RISs), the US-RIS concept by definition facilitates the employment of RIS at the user side for the first time. This is achieved by conceiving a multi-layer structure to realize a compact form-factor. Furthermore, our theoretical results demonstrate that, in contrast to the existing single-layer structure, where only the phase of the signal reflected from RIS can be adjusted, the amplitude of the signal penetrating multi-layer US-RIS can also be partially controlled, which brings about a new degree of freedom (DoF) for beamformer design that can be beneficially exploited for performance enhancement. In addition, based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem of US-RIS-aided communications. Due to the non-convexity of the problem introduced by this multi-layer structure, we propose a multi-layer transmit beamformer design relying on an iterative algorithm for finding the optimal solution by alternately updating each variable. Finally, our simulation results verify the superiority of the proposed multi-layer US-RIS as a compact realization of a large-scale antenna array at the user side for uplink transmission.

Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network’s sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.

A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS’s PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user’s throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on- board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.

The integration of terrestrial and satellite communications (Satcom) is advocated for satisfying the challenging requirements of seamless, high-performance services. However, both the bandwidth and the power available are limited over satellite channels. In this paper, we propose index modulation (IM) and code-aided Satcom by conveying information by a pair of distinguishable constellation modes and their permutations. In order to combat both the linear and nonlinear distortion imposed by satellite channels, we conceive a factor graph (FG)-based iterative detection algorithm for Satcom relying on dual-mode (DM) IM (Sat-DMIM). The correlation amongst Sat-DMIM symbols imposed by both the channel-induced dispersion and the mode-selection mapping is explicitly represented by the FG constructed. Then the amalgamated belief propagation (BP) and mean field (MF) message passing algorithm is derived over this FG for detecting both the IM bits and the classic constellation mapping bits, while eliminating both the linear and nonlinear distortions. The complexity of the iterative detection algorithm is reduced by linearizing some high-order terms appearing in nonlinear distortion components using thea posterioriestimates of the Sat-DMIM symbols obtained from the previous iteration. Our simulation results demonstrate the power of the proposed amalgamated BP-MF-based and partial linearization approximation-based iterative detection algorithms.

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasi-static single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

A sparse channel state information (CSI) estimation model is proposed for reducing the pilot overhead of orthogonal time frequency space (OTFS) modulation aided multiple-input multiple-output (MIMO) systems. Explicitly, the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI that leads to a reduction of the pilot overhead, training duration and pre-processing complexity. Furthermore, it completely avoids placing multiple DD-domain guard intervals corresponding to each transmit antenna within the same OTFS frame, while keeping the training duration flexible, hence increasing the bandwidth efficiency. A unique benefit of the proposed CSI estimation model is that it can efficiently handle fractional Dopplers also. The resultant DD-domain CSI becomes simultaneously row and group (RG)-sparse. To exploit this compelling property, an orthogonal matching pursuit (OMP)-based RG-OMP technique is developed, conveniently complemented by an enhanced Bayesian learning (BL)-based RG-BL framework, both of which substantially outperform the state-of-the-art methods. Furthermore, low-complexity linear detectors are designed for the ensuing data detection phase, which directly employ the estimated DD-domain sparse CSI, without assuming any further knowledge concerning the number of dominant multipath components. Finally, simulation results are provided to demonstrate performance improvement of the proposed BL-based schemes over the OMP and the state-of-the-art schemes.

Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes.

In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

Lower earth orbit (LEO) satellites play an important role in the integration of space and terrestrial communication networks, which typically encounter high-mobility scenarios. It has been shown that orthogonal time frequency space (OTFS) modulation performs well in such high-mobility scenarios by transforming the time-varying channels into the delay-Doppler domain. In this paper, we develop a joint channel estimation and data detection algorithm for OTFS-based LEO satellite communications. Firstly, we adopt the powerful variational Bayesian inference (VBI) method for estimating the delay-Doppler channel vector, which contains the channel gain, the delay and the Doppler. Secondly, we exploit the unknown data symbols in an OTFS frame as ‘virtual pilots’ for improving the accuracy of channel estimation and detect them simultaneously. Our simulation results demonstrate that the proposed algorithm achieves improved channel estimation mean square error and bit error rate performance than its conventional counterparts.

The hybrid nonlinear transceiver optimization problem of reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MU-MIMO) downlink is investigated. Specifically, the Tomlinson-Harashima precoder (THP) and the hybrid transmit precoder (TPC) of the base station are jointly optimized with the linear digital receivers of mobile users. The triangular feedback matrix of the THP is optimized and the optimal solution is derived in closed form based on a matrix inequality. Moreover, in order to tackle the nonconvexity of the constant-modulus constraints imposed on the analog TPC, the Majorization-Minimization (MM) based reconfigurable optimization framework is proposed, which strikes a trade-off between the implementation complexity and system performance in a reconfigurable manner. Explicitly, our MM-based reconfigurable optimization framework is capable of optimizing the analog TPC in a dynamically reconfigurable manner on an element-by-element, column-by-column, row-by-row or block-by-block basis. Moreover, an MM-based reconfigurable algorithm is proposed for the optimization of the phase shifting matrix at RIS, which also suffers from constant-modulus constraints. In the proposed MM-based reconfigurable algorithm, the RIS can be partitioned into a series of subarrays for striking different performance vs. complexity tradeoffs. Finally, our numerical results demonstrate the performance advantages of the proposed nonlinear hybrid transceiver optimization techniques.

The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.

Quantum error mitigation (QEM) is a class of promising techniques capable of reducing the computational error of variational quantum algorithms tailored for current noisy intermediate-scale quantum computers. The recently proposed permutation-based methods are practically attractive, since they do not rely on anya prioriinformation concerning the quantum channels. In this treatise, we propose a general framework termed as permutation filters, which includes the existing permutation-based methods as special cases. In particular, we show that the proposed filter design algorithm always converge to the global optimum, and that the optimal filters can provide substantial improvements over the existing permutation-based methods in the presence of narrowband quantum noise, corresponding to large-depth, high-error-rate quantum circuits.

Quantum error mitigation (QEM) is a class of promising techniques for reducing the computational error of variational quantum algorithms. In general, the computational error reduction comes at the cost of a sampling overhead due to the variance-boosting effect caused by the channel inversion operation, which ultimately limits the applicability of QEM. Existing sampling overhead analysis of QEM typically assumes exact channel inversion, which is unrealistic in practical scenarios. In this treatise, we consider a practical channel inversion strategy based on Monte Carlo sampling, which introduces additional computational error that in turn may be eliminated at the cost of an extra sampling overhead. In particular, we show that when the computational error is small compared to the dynamic range of the error-free results, it scales with the square root of the number of gates. By contrast, the error exhibits a linear scaling with the number of gates in the absence of QEM under the same assumptions. Hence, the error scaling of QEM remains to be preferable even without the extra sampling overhead. Our analytical results are accompanied by numerical examples.

We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as “spurious” or “abnormal” in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves. To quantify the similarity betweenunknowntesting samples and thegiventraining samples (including normal samples), we first propose a so-calleddeviation-based algorithm. Furthermore, we estimate the angle of arrival (AoA) from each legitimate aeroplane and propose a so-calledAoA-based algorithm. Then based on a sophisticated amalgamation of these two algorithms, we form our final detection algorithm for distinguishing the spurious abnormal samples from normal samples under a strict testing condition. In conclusion, our numerical results show that the AE improves the trade-off between the correct spoofing detection rate and the false alarm rate as long as the detection thresholds are carefully selected.

The physical layer security (PLS) of millimeter wave (mmWave) communication systems is investigated, where the secure source-to-destination communication is assisted by an untrusted relay selected from a group of them and there are also several passive eavesdroppers (Eves) in the network. In the considered system model, while the distributions of the untrusted relays and Eves follow a homogeneous Poisson Point Process (PPP). To maximize the instantaneous secrecy rate, a novel joint relay selection and power allocation (JRP) method is developed where the destination and source aim for jamming the reception of both the untrusted relays and passive Eves. New expressions of the optimal power allocation (OPA) are derived for both non-colluding Eves (NCE) and colluding Eves (CE). Subsequently, by considering the impact of potential blockages, new closed-form equations are derived for analyzing the system’s ergodic secrecy rate (ESR) and secrecy outage probability (SOP) for transmission over fading mmWave channels. Finally, numerical examples are provided for demonstrating the superiority of our proposed JRP method over the relevant benchmarks found in the literature. Interestingly, the ESR increases with the density of untrusted relays for both the NCE and CE scenarios, which is a benefit of the improved probability of selecting a relay with a stronger second-hop channel. Furthermore, in the low transmit power regime, employing relatively low mmWave frequencies achieves better ESR, while in the high transmit power regime, high mmWave frequencies provide higher ESR.

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices.

Orthogonal time frequency space (OTFS) waveform based millimeter wave (mmWave) MIMO systems are capable of achieving high data rates in high-mobility scenarios. Hence, transceivers are designed for both analog beamforming (AB) and hybrid beamforming (HB), where we commence by deriving the delay-Doppler (DD)-domain input-output relationship considering a delay-Doppler-angular domain channel model. Subsequently, a novel two-stage procedure is developed for transmit beamformer (TBF)/ precoder (TPC) and receiver combiner (RC) design, and for estimating the DD-domain’s equivalent channel state information (CSI). The key feature of the proposed framework is that the RF TBF/ TPC and RC design maximizes the directional beamforming gains. It is also demonstrated that the low-dimensional baseband CSI of the DD-domain becomes sparse for mmWave-AB MIMO OTFS systems, and block-sparse for mmWave-HB MIMO OTFS systems. Subsequently, Bayesian learning (BL) and block-sparse BL (BS-BL) solutions are developed for improved CSI estimation. We also derive the Bayesian Cramer-Rao lower bounds (BCRLB) for benchmarking the mean-squared-error (MSE) of the CSI estimates. Finally, our simulation results demonstrate the improved efficacy of the proposed transceiver designs and confirm the enhanced CSI estimation performance of the BL-based schemes over other competing sparse signal recovery schemes.

This paper considers a reconfigurable intelligent surface (RIS)-aided network, which relies on a multiple antenna array aided base station (BS) and an RIS for serving multiple single antenna downlink users. To provide reliable links to all users over the same bandwidth and same time-slot, the paper proposes the joint design of linear transmit beamformers and the programmable reflecting coefficients of an RIS to maximize the geometric mean (GM) of the users’ rates. A new computationally efficient alternating descent algorithm is developed, which is based on closed-forms only for generating improved feasible points of this nonconvex problem. We also consider the joint design of widely linear transmit beamformers and the programmable reflecting coefficients to further improve the GM of the users’ rates. Hence another alternating descent algorithm is developed for its solution, which is also based on closed forms only for generating improved feasible points. Numerical examples are provided to demonstrate the efficiency of the proposed approach.

This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs’ harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs’ harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs’ throughput.

Over-the-air computation (AirComp) has received substantial attention, given its ability to aggregate massive amounts of data from distributed wireless devices (WDs). However, the computation accuracy at the fusion center (FC) may be severely affected by receiving data corrupted by the poor channel conditions. To mitigate this issue, we consider the employment of reconfigurable intelligent surfaces (RISs) in the AirComp system considered for improving the quality of received data, and hence improve the computation accuracy. However, most previous contributions on RIS-assisted AirComp systems only employ a single RIS in the resultant single-RIS-assisted (SRIS-assisted) AirComp systems. We develop this concept further for mitigating the deleterious channel effects by conceiving a double-RIS-assisted (DRIS-assisted) AirComp system, where one of the RISs is located near the WDs and the other in the vicinity of the FC. We theoretically prove that the DRIS-assisted AirComp system outperforms its SRIS-assisted counterpart in terms of the resultant computation mean-squared-error (MSE). Furthermore, we propose a pair of algorithms for jointly optimizing the transmit power at the WDs, the receive beamforming vector at the FC, and the passive beamforming matrices at the RISs for minimizing the computational MSE. Specifically, the transmit power is updated by exploiting the Lagrange duality method, while the receive beamforming vector is optimized by utilizing the first-order optimality condition. Furthermore, a pair of techniques are developed for optimizing the passive beamforming matrices at the RISs based on semidefinite relaxation (SDR) and penalty-duality-decomposition (PDD), respectively. Both the complexity and the convergence of the proposed algorithms are analyzed. Finally, simulation results are provided for quantifying the overall performance of the resultant DRIS-assisted AirComp system.

Thanks to the spectacular advances in signal processing and nano-technology, five wireless generations have been conceived over the past five decades. Indeed, near-capacity operation at an infinitesimally low error-rate has become feasible and flawless multimedia communications is supported in areas of high traffic-density, but how do we fill the huge coverage holes existing across the globe? As a promising system-architecture, the SAGIN concept constituted by an integrated terrestrial, UAV-aided, airplane-assisted as well as satellite-based global coverage-solution will be highlighted to pave the way for seamless next-generation service provision. However, these links exhibit strongly heterogeneous properties, hence requiring different enabling techniques. The joint optimization of the associated conflicting performance metrics of throughput, transmit power, latency, error probability, hand-over probability and link-lifetime poses an extremely challenging problem. Explicitly, sophisticated multi-component system optimization is required for finding the Pareto-front of all optimal solutions, where none of the above-mentioned metric can be improved without degrading at least one of the others [1] - [5]....

Index modulation (IM) has been recognized as a compelling candidate for the next generation wireless networks. This paper considers index modulation-orthogonal frequency division multiplexing (IM-OFDM) with single subcarrier activation, where a single subcarrier is activated for transmitting an amplitude-phase modulated (APM) symbol in each IM group. Furthermore, a reduced-complexity approximate message passing aided symbol detector (AMPD) is proposed for reducing the detection complexity at the receiver side. The system performance of IM-OFDM with single subcarrier activation is investigated in terms of spectral efficiency (SE), peak-to-average power ratio (PAPR), bit error ratio (BER), as well as detection complexity. Our studies show that IM-OFDM with single subcarrier activation is capable of attaining low PAPR. Moreover, IMOFDM with single subcarrier activation outperforms the classic OFDM using APM for a SE of 1 bit/s/Hz, when high power amplifier (HPA) is employed. Furthermore, our investigation shows that low detection complexity can be achieved by the proposed AMPD for IM-OFDM with single subcarrier activation at the cost of a certain BER performance loss.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

Intelligent reflecting surface (IRS) is capable of constructing the favorable wireless propagation environment by leveraging massive low-cost reconfigurable reflect array elements. In this article, we investigate the IRS-aided multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) for Internet-of-Things (IoT) networks, where the active base station (BS) transmits beamforming and the passive IRS reflection coefficients are jointly optimized for maximizing the minimum signal-to-interference-plus-noise ratio (SINR) among all information decoders (IDs), while maintaining the minimum total harvested energy at all energy receivers (ERs). Moreover, the IRS with practical discrete phase shifts is considered, and thereby the max-min SINR problem becomes an NP-hard combinatorial optimization problem with a strong coupling among optimization variables. To explore the insights and generality of this max-min design, both the single-ID single-ER (SISE) scenario and the multiple-IDs multiple-ERs (MIME) scenario are studied. In the SISE scenario, the classical combinatorial optimization techniques, namely, the special ordered set of type 1 (SOS1) and the reformulation-linearization (RL) technique, are applied to overcome the difficulty of this max-min design imposed by discrete optimization variables. Then, the optimal branch-and-bound algorithm and suboptimal alternating optimization algorithm are, respectively, proposed. We further extend the idea of alternating optimization to the MIME scenario. Moreover, to reduce the iteration complexity, a two-stage scheme is considered aiming to separately optimize the BS transmit beamforming and the IRS reflection coefficients. Finally, numerical simulations demonstrate the superior performance of the proposed algorithms over the benchmarks in both the two scenarios.

To circumvent the degraded goodput, high latency and low resource utilization efficiency of conventional transport-layer protocols in the Internet of Things, a novel permutation-based encapsulation scheme is proposed for seamless delivery of packets to achieve high goodput and low latency. In this scheme, extra application-layer data is delivered by our permutation-based data unit (PBDU), which is mapped onto the permutation associated with a legitimate tuple of various packet lengths in a group. Therefore, the network interface throughput is substantially improved for a given number of resource units in the physical channel. The optimal design of this encapsulation is expressed in closed form to maximize the PBDU size, allowing us to quantify the attainable improvements of goodput and latency. In addition, the proposed scheme achieves higher physical-layer throughput and secrecy rate than conventional encapsulation.

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultra-dense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decision process (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge caches.

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory,Theorem 1presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically,Theorem 1fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applyingTheorem 1to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure inTheorem 1and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

TeraHertz (THz) wireless communication constitutes a promising technique of satisfying the ever-increasing appetite for high-rate services. However, the ultra-wide bandwidth of THz communications requires high-speed, high-resolution analog-to-digital converters, which are hard to implement due to their high complexity and power consumption. In this paper, a deep learning-assisted THz receiver is designed, which relies on single-bit quantization. Specifically, the imperfections of THz devices, including their in-phase/quadrature-phase imbalance, phase noise and nonlinearity are investigated. The deflection ratio of the maximum-likelihood detector used by our single-bit-quantization THz receiver is derived, which reveals the effect of phase offset on the demodulation performance, guiding the architecture design of our proposed receiver. To combat the performance loss caused by the above-mentioned distortions, a twin-phase training strategy and a neural network based demodulator are proposed, where the phase offset of the received signal is compensated before sampling. Our simulation results demonstrate that the proposed deep learning-assisted receiver is capable of achieving a satisfactory bit error rate performance, despite the grave distortions encountered.

Conventional hybrid precoding and combining based transceivers require a large number of high-resolution radio frequency (RF) phase shifters (PSs), which impose prohibitive hardware costs and power consumption. To address the above issue, both partially connected RF PSs and low-resolution PSs have been proposed. However, the performance limits of these low-cost designs have not been investigated theoretically. Furthermore, there is room for improvement in their spectral efficiency. To fill this knowledge gap, we derive the mean square error performance discrepancy between an optimal precoder/combiner and the hybrid analog-digital precoder/combiner under the constraint of 1-bit PSs relying on lattice theory. Then, by observing that this performance gap can be reduced by deactivating parts of the PSs whilst improving both the spectral and energy efficiency, we develop an adaptive RF PS connection network. To resolve the associated hybrid precoding and combining problems, we appropriately adapt Babai's algorithm from the lattice decoding literature. Our simulation results demonstrate the superiority of the proposed scheme both in terms of its spectral and energy efficiency.

A low-complexity parametric bilinear generalized approximate message passing (PBiGAMP)-based receiver is conceived for multi-carrier faster-than-Nyquist (MFTN) signaling over frequency-selective fading channels. To mitigate the inherent ill-conditioning problem of MFTN signaling, we construct a segment-based frequency-domain received signal model in the form of a block circulant linear transition matrix, which can be efficiently calculated by applying a two dimensional fast Fourier transform. Based on the eigenvalue decomposition of the block circulant matrices, we can diagonalize the covariance matrix of the complex-valued colored noise process imposed by the associated two dimensional non-orthogonal matched filtering. Building on this model, a PBiGAMP-based parametric joint channel estimation and equalization (JCEE) algorithm is proposed for MFTN systems. In this algorithm, we introduce a pair of additive terms for characterizing the interferences arising from adjacent segments and employ the exact discretea prioriprobabilities of the transmitted symbols for improving the bit error rate (BER) performance. To further enhance the system’s robustness in the presence of ill-conditioned matrices, we develop a refined PBiGAMP-based JCEE algorithm by introducing a series of scaled identity matrices. Moreover, the proposed PBiGAMP-based JCEE algorithms may be readily decomposed into GAMP-based equalization algorithms, when the channel state information is perfectly known. The overall complexity of the proposed algorithms only increases logarithmically with the total number of transmitted symbols. Our simulation results demonstrate the benefits of the proposed PBiGAMP-based iterative message passing receiver conceived for MFTN signaling.

The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas.

We investigate massive access in the Internet-of-Things (IoT) relying on multi-pair two-way amplify-and-forward (AF) relay systems using massive multiple-input multiple-output (MIMO). We utilize the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation. Furthermore, we analyze the achievable rates for multiple pairs of active devices and derive the closed-form expressions for both maximum-ratio combining/maximum-ratio transmission (MRC/MRT) and zero-forcing reception/zero-forcing transmission (ZFR/ZFT)-based beamforming schemes adopted at the relay. Moreover, to improve the achievable sum rates, we propose a low-complexity algorithm for optimizing the pilot length L. Our simulation results verify the accuracy of the closed-form expressions of the MRC/MRT and ZFR/ZFT scenarios. Finally, the proposed pilot-length optimization algorithm performs well in both the MRC/MRT and ZFR/ZFT scenarios.

An optimal precoder design is conceived for the decentralized estimation of an unknown spatially as well as temporally correlated parameter vector in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based wireless sensor network (WSN). Furthermore, exploiting the temporal correlation present in the parameter vector, a rate-distortion theory based framework is developed for the optimal quantization of the sensor observations so that the resultant distortion is minimized for a given bit-budget. Subsequently, optimal precoders are also developed that minimize the sum-MSE (SMSE) for the scenario of transmitting quantized observations. In order to reduce the computational complexity of the decentralized framework, distributed precoder design algorithms are also developed which design precoders using the consensus based alternating direction method of multipliers (ADMM), wherein each SN determines its precoders without any central coordination by the fusion center. Finally, new robust MIMO precoder designs are proposed for practical scenarios operating in the face of channel state information (CSI) uncertainty. Our simulation results demonstrate the improved performance of the proposed schemes and corroborate our analytical formulations.

Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relying on realistic imperfect channel estimates over the benchmarks.

Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs' information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach.

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier's quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel's frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramér-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.

An edge-cancellation-aided iterative detection and decoding (EC-IDD) algorithm is proposed for polar-coded sparse code multiple access (SCMA), which jointly performs Gaussian-approximated message passing (GA-MP) detection of SCMA supported by the soft list decoding (SLD) of polar codes. A reduced-edge factor graph is formulated in each consecutive iteration with the aid of the cyclic redundancy check (CRC) and EC. Based on the simplified factor graph, the EC-IDD gradually reduces its complexity in each subsequent iteration, while improving the bit error rate (BER) performance, compared to the state-of-the-art joint detection and decoding (JDD) of polar-coded SCMA. Furthermore, an embedded decision-directed channel estimator (DD-CE) is proposed for our polar-coded SCMA system under realistic imperfect channel state information (CSI). Our simulation results demonstrate that the proposed EC-IDD achieves better BER performance than the state-of-the-art JDD under both perfect and imperfect CSI, despite achieving a complexity reduction of 92%. Finally, the BER of the proposed joint DD-CE and EC-IDD algorithm under imperfect CSI converges to that of EC-IDD operating under perfect CSI.

An unmanned aerial vehicle (UAV)-aided secure communication system is conceived and investigated, where the UAV transmits legitimate information to a ground user in the presence of an eavesdropper (Eve). To guarantee the security, the UAV employs a power splitting approach, where its transmit power can be divided into two parts for transmitting confidential messages and artificial noise (AN), respectively. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory, the transmit power levels and the corresponding power splitting ratios allocated to different time slots during the whole flight time, subject to both the maximum UAV speed constraint, the total mobility energy constraint, the total transmit power constraint, and other related constraints. To efficiently tackle this non-convex optimization problem, we propose an iterative algorithm by blending the benefits of the block coordinate descent (BCD) method, the concave-convex procedure (CCCP) and the alternating direction method of multipliers (ADMM). Specially, we show that the proposed algorithm exhibits very low computational complexity and each of its updating steps can be formulated in a nearly closed form. Besides, it can be easily extended to the case of three-dimensional (3D) trajectory design. Our simulation results validate the efficiency of the proposed algorithm.

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this article, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages' SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel's Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only has a single one.

Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS-transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two-stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems.

A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then ‘beamformed’ by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the ‘intra-content interference’ is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.

Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper (Eve). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external Eve, we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out Eve without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the Eve, deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.

Millimeter wave (mmWave) communications and cognitive radio technologies constitute key technologies of improving the spectral efficiency of communications. Hence, we conceive a hybrid secure precoder for enhancing the physical layer security of a cognitive mmWave wiretap channel, where a secondary transmitter broadcasts confidential information signals to multiple secondary users under the interference temperature constraint of the primary user (PU). The optimization problem is formulated as jointly optimizing the analog and digital precoder for maximizing the minimum secrecy rate of all the secondary users under practical constraints. In particular, our design satisfies the constraint on the maximum interference power received by multiple PUs, as well as the secondary users’ minimum quality-of-service (Qos), and the unit-modulus constraint on the analog precoder. Due to the non-convexity of the resultant objective function and owing to the coupling between the analog and digital precoder, the optimization problem formulated is nonconvex and nonlinear, hence it is very challenging to solve directly. Hence, we first transform it into a tractable form, and develop a penalty dual decomposition (PDD) based iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we generalize the proposed PDD algorithm to a secure hybrid precoder design relying on practical finite-resolution phase shifters and show that the proposed PDD algorithm can be straightforwardly adapted to handle the scenario, where each PU is equipped with multiple antennas and the CSI of multiple eavesdroppers (Eves) is imperfectly known. Our simulation results validate the efficiency of the proposed iterative algorithm.

Providing security guarantee is a critical concern in the ad-hoc networks relying on multi-hop channels, since their flexible topology is vulnerable to security attacks. To enhance the security of a spatial modulation (SM) assisted wireless network, various SM mapping patterns are activated by random channel quality indicator (CQI) patterns over the legitimate link, as a physical-layer secret key. The SM signals are encrypted by random mapping patterns to prevent eavesdroppers from correctly demapping their detections. This secret key is developed for multi-hop wiretap ad-hoc networks, where eavesdroppers might monitor all the transmitting nodes of a legitimate link. We substantially characterise the multi-hop wiretap model with receiver diversity techniques adopted by eavesdroppers. The security performance of the conceived scheme is evaluated in the scenarios where eavesdroppers attempt to detect their received signals using maximal-ratio combining or maximum-gain selection. The achievable data rates of both legitimate and wiretapper links are formulated with the objective of quantifying the secrecy rates for both Gaussian-distributed and finite-alphabet inputs. Illustrative numerical results are provided for the metrics of ergodic secrecy rate and secrecy outage probability, which substantiate the compelling benefits of the physical-layer secret key generation via CQI-mapped SM.

Distributed parameter detection is conceived for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs), where multiple sensors collaborate to detect the presence/ absence of a spatially correlated parameter. Neyman-Pearson (NP) and generalized likelihood ratio test (GLRT)-based detectors are developed at the fusion center (FC) for known and unknown parameter detection scenarios, respectively. More explicitly, the GLRT detector also has to estimate the unknown parameter value. Closed-form expressions are derived for the probabilities of detection (PD) and false alarm (PFA) in order to characterize the performance of the proposed schemes. Furthermore, the optimal sensor transmit gains are determined for maximising the detection performance attained. An asymptotic performance analysis is carried out for determining the gain scaling laws for the massive MIMO WSN considered, when the number of antennas tends to infinity. The proposed framework is also extended to the realistic imperfect channel knowledge scenario at the FC, followed by the development of the associated fusion rules and analytical results to characterize the performance. Our simulation results closely tally the theoretical findings.

Hybrid Transmit Precoding (TPC) is one of the most compelling solutions for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems. However, most attention has been focused on narrow-band scenarios. Hence, we dedicate our efforts to the design of hybrid TPC for wideband mmWave MIMO systems, where the beam squint dramatically affects the system performance. We firstly show that the channel matrices of the different subcarriers possess distinct subspaces in case of high bandwidths, hence traditional hybrid TPC schemes suffer from an eroded performance. Therefore, we propose novel hybrid TPC schemes exploiting the full channel state information (CSI), which project all frequencies to the central frequency and construct the common analog TPC matrix for all subcarriers. Moreover, we propose several low-complexity array-vector based hybrid TPC schemes. The high-complexity manifold optimization based hybrid TPC method and the fully digital TPC operating with and without considering beam squint are provided as benchmarks. Our extensive numerical simulations show that the proposed hybrid TPC schemes are capable of achieving similar performance to the excessive-complexity fully digital TPC, when the bandwidth tends to 0.5 GHz and always outperform the traditional hybrid TPC schemes.

Hybrid analog/digital precoding in millimeter-wave (mmWave) multi-input multi-ouput (MIMO) systems is capable of achieving the near-optimal full-digital performance at reduced hardware cost and power consumption compared to its full-RF digital counterpart. However, having numerous phase shifters is still costly, especially when the phase shifters are of high resolution. In this paper, we propose a novel twin-resolution phase-shifter network for mmWave MIMO systems, which reduces the power consumption of an entirely high-resolution network, whilst mitigating the severe array gain reduction of an entirely low-resolution network. The connections between the twin phase shifters having different resolutions and the antennas are either fixed or dynamically configured. In the latter, we jointly design the phase-shifter network and the hybrid precoding matrix, where the phase of each entry in the analog precoding matrix can be dynamically designed according to the required resolution. This method is slightly modified for the fixed network's hybrid precoding matrix. Furthermore, we extend the proposed method to multi-user MIMO systems and provide its performance analysis. Our simulation results show that the proposed dynamic hybrid precoding method strikes an attractive performance vs. power consumption trade-off.

Millimeter wave communication is eminently suitable for high-rate wireless systems, which may be beneficially amalgamated with intelligent reflecting surfaces (IRS), while relying on beam-index modulation. Explicitly, we propose three different architectures based on IRSs for beam-index modulation in millimeter wave communication. Our schemes are capable of eliminating the detrimental line-of-sight blockage of millimeter wave frequencies.The schemes are termed as single-symbol beam index modulation, multi-symbol beam-index modulation and maximum-SNR single-symbol beam index modulation. The principle behind these is to embed the information both in classic QAM/PSK symbols and in the transmitter beam-pattern. Explicitly, we proposed to use a twin-IRS structure to construct a low-cost beam-index modulation scheme. We conceive both the optimal maximum likelihood detector and a low-complexity compressed sensing detector for the proposed schemes. Finally, the schemes designed are evaluated through extensive simulations and the results are compared to our analytical bounds.

Space-time coded generalized spatial modulation-aided sparse code division multiple access (STC/GSM-SCDMA) is proposed, which exploits the two-dimensional transmit diversity potential of both the spatial and of the frequency domain. Hence, it constitutes a promising solution for the pervasive connectivity of devices in next-generation nonorthogonal multiple access (NOMA) systems. More explicitly, our STC/GSM scheme achieves diversity in the spatial-domain, while the sparse signal-spreading action of SCDMA results in frequency-domain (FD) diversity. A single-user bit error rate (BER) bound is derived as the benchmark of the BER performance of our STC/GSM-SCDMA system. Furthermore, a pair of novel detectors, namely a bespoke message passing aided (MPA) detector and a tailor-made approximate message passing (AMP) detector are conceived by designing a new factor graphs for our proposed STC/GSM-SCDMA system. The performance of these detectors is characterized in terms of their BER vs. complexity. Our simulation results show that the proposed AMP detector is capable of operating within 2 dB of the MPA detector's signal-to-noise ratio (SNR) requirement, while supporting a normalized user load of 150%, despite its appealing low complexity, which is about 1000 times lower than the MPA detector.

Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption of each base station over a complete video streaming session under the constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive power allocation policies that first predict future information with historical data and then optimize the power allocation based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, we reduce the signaling overhead between the edge server and the base stations, and make it easier to learn a good policy. To further avoid playback interruption throughout the learning process and improve the convergence speed, we exploit the partially known model of the system dynamics by integrating the concepts of safety layer, post-decision state, and virtual experiences into the basic DDPG algorithm. Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect large-scale channel prediction and outperform the first-predict-then-optimize policy in the presence of prediction errors. By harnessing the partially known model, the convergence speed can be dramatically improved. The code for reproducing the results of this article is available at https://github.com/fluidy/twc2020.

Intelligent reflecting surfaces (IRSs) improve both the bandwidth and energy efficiency of wideband communication systems by using low-cost passive elements for reflecting the impinging signals with adjustable phase shifts. To realize the full potential of IRS-aided systems, having accurate channel state information (CSI) is indispensable, but it is challenging to acquire, since these passive devices cannot carry out transmit/receive signal processing. The existing channel estimation methods conceived for wideband IRS-aided communication systems only consider the channel’s frequency selectivity, but ignore the effect of beam squint, despite its severe performance degradation. Hence we fill this gap and conceive wideband channel estimation for IRS-aided communication systems by explicitly taking the effect of beam squint into consideration. We demonstrate that the mutual correlation function between the spatial steering vectors and the cascaded two-hop channel reflected by the IRS has two peaks, which leads to a pair of estimated angles for a single propagation path, due to the effect of beam squint. One of these two estimated angles is the frequency-independent ‘actual angle’, while the other one is the frequency-dependent ‘false angle’. To reduce the influence of false angles on channel estimation, we propose a twin-stage orthogonal matching pursuit (TS-OMP) algorithm, where the path angles of the cascaded two-hop channel reflected by the IRS are obtained in the first stage, while the propagation gains and delays are obtained in the second stage. Moreover, we propose a bespoke pilot design by exploiting the specific the characteristics of the mutual correlation function and cross-entropy theory for achieving an improved channel estimation performance. Our simulation results demonstrate the superiority of the proposed channel estimation algorithm and pilot design over their conventional counterparts.

Massive machine-type communication (mMTC) is expected to provide reliable and low-latency connectivity for an enormous number of devices, which turn active sporadically or frequently. In this highly dynamic situation, it is crucial to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this article, by exploiting the large sequence space, the excellent correlation property and especially the elegant nested structure of Reed-Muller (RM) sequences, we propose a new RA scheme, which facilitates both instantaneous access for newly active users and incremental access for the existing users who suffer from detection failures. In particular, when a failure occurs, the user continues accessing the channel employing an expanded RM sequence, which is combined with the previously received ones at the access point (AP) to form a longer sequence so as to attain potentially better detection probability. Furthermore, a recursive detection algorithm is designed for jointly detecting the resultant RM sequences and the channel coefficients of both the newly active users and the existing ones. The performance of the proposed algorithm is analyzed in detail. Our simulation results validate the analysis and show the scheme's superior access probability, access latency and computational complexity.

As the enabling technologies move up to the mmWave and even to the TeraHertz bands for the next-generation wireless systems, the signal processing of high-bandwidth orthogonal frequency division multiplexing (OFDM) becomes increasingly power-thirsty, owing to the following OFDM deficiencies: (1) the high peak-to-average power ratio (PAPR); (2) the bandwidth efficiency loss due to the cyclic prefix (CP) overhead; (3) the sensitivity to carrier frequency offset; (4) the complex out-of-band (OOB) filtering. Over the past six decades, a variety of waveforms have been developed in order to mitigate these deficiencies, which are generally achieved at the cost of compromising some of OFDM’s beneficial properties, such as its subcarrier (SC) orthogonality, its high throughput and its straighforward adoption to multiple-input multiple-output (MIMO) systems. Against this background, we propose a new waveform termed as multi-band discrete Fourier transform spread-OFDM with index modulation (MB-DFT-S-OFDM-IM), where the component multi-carrier techniques are conceived to constructively function together in order to mitigate the OFDM deficiencieswithout compromising the beneficial OFDM properties. More explicitly, first of all, the PAPR is reduced by the DFT-precoding. Secondly, thanks to the IM design, MB-DFT-S-OFDM-IM is capable of achieving a high throughput that is strictly equal to or higher than the OFDM throughput. Thirdly, MB-DFT-S-OFDM-IM achieves a beneficial frequency diversity gain, which leads to a higher tolerance to carrier frequency offset. Fourthly, the OOB filters are placed in each sub-band before DFT, so that the SC orthogonality remains intact, which is unique to the proposed MB-DFT-S-OFDM-IM structure. Last but not least, we extend the proposed MB-DFT-S-OFDM-IM to support a variety of MIMO schemes, where the IM philosophy is integrated with the space-, time- and frequency-domains within a singleunifiedplatform.

Both faster than Nyquist (FTN) signaling and non-orthogonal multiple access (NOMA) are promising next generation wireless communications techniques as a benefit of their capability of improving the system's spectral efficiency. This paper considers an uplink system that combines the advantages of FTN and NOMA. Consequently, an improved spectral efficiency is achieved by deliberately introducing both inter-symbol interference (ISI) and inter-user interference (IUI). More specifically, we propose a grant-free transmission scheme to reduce the signaling overhead and transmission latency of the considered NOMA system. To distinguish the active and inactive users, we develop a novel message passing receiver that jointly estimates the channel state, detects the user activity, and performs decoding. We conclude by quantifying the significant spectral efficiency gain achieved by our amalgamated FTN-NOMA scheme compared to the orthogonal transmission system, which is up to 87.5%.

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and/or enhancing its capacity. Hence we investigate a mmWave device-to-device (D2D) MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). In order to minimize the system's delay, the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, over orthogonal time slots. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ A/D hybrid beamforming for their transmission. We develop a novel algorithm for jointly optimizing the offloading ratio and the hybrid beamformers. The simulation results show that the proposed algorithm significantly reduces the system's delay compared to the existing algorithms.

Caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. In this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.

In this article, relay-assisted computation offloading (RACO) is investigated, where user A wishes to share the results of computational tasks with another user B with the assistance of a mobile-edge relay server (MERS). To enable this computation offloading, we propose a hybrid relaying (HR) approach employing a pair of orthogonal frequency bands, which are, respectively, used for the amplify-forward relaying of computational results and the decode-forward relaying of the unprocessed raw tasks. The motivation here is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Using this framework, we seek to minimize the weighted sum of the execution delays and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user A and the MERS, under some practical constraints. By adopting a series of transformations, we first recast this problem into a form amenable to optimization and then develop an efficient iterative algorithm for its solution based on the concave-convex procedure (CCCP). By virtue of the particular problem structure in our case, we propose furthermore a simplified algorithm based on the inexact block coordinate descent (IBCD) method, which leads us to much lower computational complexity. Finally, our numerical results demonstrate the advantages of the proposed algorithms over the state-of-the-art benchmark schemes.

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale analog-digital hybrid beamforming scheme for maximizing the sum rate, while reducing the system's complexity and the channel state information (CSI) signalling overhead, as well as mitigating both the effects of self-interference and that of outdated CSIs caused by the associated delays. In the proposed scheme, the long-timescale analog beamforming matrices are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. By contrast, the short-timescale digital beamforming matrices are optimized more frequently - namely for each time slot - based on the low-dimensional effective CSI matrices available on a real-time basis. We develop both an efficient analog beamforming algorithm based on the cut-set bound as well as on stochastic successive convex approximation (SSCA) and an innovative digital beamforming algorithm that relies on the theory of penalty dual decomposition (PDD), where our design objective is to maximize the system's sum rate. Both the convergence properties and the computational complexity of the proposed algorithms are also examined. Our simulation results show that the proposed two-timescale hybrid beamforming design significantly outperforms the conventional beamformers both in terms of requiring a lower CSI-signalling overhead and a higher sum rate in the face of realistic outdated CSIs.

Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signal-to-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

Seven decades after the foundation of classical information theory and the invention of the transistor that launched the digital communication and computing revolutions, we are entering a new era of quantum information science and engineering (QISE). Despite holding its impressive sway for nearly 60 years, the celebrated Moore’s law is beginning to hit physical limits, as the ever-shrinking transistor size is making it necessary to account for quantum effects. Concurrently, the growing demand for high-rate processing is imposing unsustainable power and heat dissipation requirements. Thus, there is an urgent need to develop quantum information processing systems that can circumvent the limitations of existing technology.

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

A finite blocklength (FBL) twin-user non-orthogonal cooperative downlink system is considered, wherein a base station simultaneously communicates with the users whilst relying on simultaneous wireless information and power transfer (SWIPT) enabled energy harvesting relay. Closed-form analytical expressions are obtained for the end-to-end average block error rate (BLER) assuming potentially dissimilar Nakagami-m fading links. Furthermore, asymptotic expressions are derived for the individual user's end-to-end average BLER performance and diversity order at high signal to noise ratios (SNRs). Additionally, since, the stronger user's signal is detected, while assuming the weaker user's signal to be interference, an analytical bound has been obtained for characterizing the effect of this interference on the BLER performance of both users. Furthermore, a framework is developed for joint relay splitting factor and user power allocation towards end-to-end blocklength minimization under strict reliability constraints by converting the original non-convex optimization problem to a convex geometric program (GP) that can be solved efficiently. Simulation results are presented to validate the analytical expressions derived and illustrate the efficacy of the proposed scheme.

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user's bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users' end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under per-access point (AP) power constraints. The max-min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel's coherence time and on the total number of users.

Quantum Teleportation is the key communication functionality of the Quantum Internet, allowing the “transmission” of qubits without the physical transfer of the particle storing the qubit. Quantum teleportation is facilitated by the action of quantum entanglement, a somewhat counter-intuitive physical phenomenon with no direct counterpart in the classical word. As a consequence, the very concept of the classical communication system model has to be redesigned to account for the peculiarities of quantum teleportation. This re-design is a crucial prerequisite for constructing any effective quantum communication protocol. The aim of this manuscript is to shed light on this key concept, with the objective of allowing the reader: i) to appreciate the fundamental differences between the transmission of classical information versus the teleportation of quantum information; ii) to understand the communications functionalities underlying quantum teleportation, and to grasp the challenges in the design and practical employment of these functionalities; iii) to acknowledge that quantum information is subject to the deleterious effects of a noise process termed as quantum decoherence. This imperfection has no direct counterpart in the classical world; iv) to recognize how to contribute to the design and employment of the Quantum Internet.

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users' confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption.

A probabilistically robust transmit beamforming problem is referred, when the wireless downlink (DL) communication is supported by a robust non-orthogonal transmission (NOT)-aided design. Realistic imperfect channel state information (CSI) is considered in the face of rapidly fluctuating vehicular wireless channels, when the road side unit (RSU) communicates with multiple vehicles. Our design objective is to keep the probability of each vehicle's signal-to-interference-plus-noise ratio (SINR) outage below a given threshold. Minimizing the outage probability presents a significant analytical and computational challenge, since it does not lend itself to tractable closed-form expressions. Assuming a Gaussian CSI uncertainty distribution, we provide an approximation method by resorting to the semidefinite relaxation (SDR) and then apply a convex restriction to the original SINR outage constraints. Furthermore, the infinite constraints are reformulated into linear matrix inequalities (LMIs) by exploiting the popular S-procedure. As a benefit, the reformulated program can be solved efficiently using off-the-shelf solvers. Computer simulations are performed for benchmarking our convex method both against the non-robust non-orthogonal as well as the classical orthogonal designs. The results show that our robust beamforming design offers excellent high-mobility performance.

Conventional authentication mechanisms routinely used for validating communication devices are facing significant challenges. This is mainly due to their reliance on both `spoofable' digital credentials and static binary characteristic, and inevitable misdetection in physical layer authentication using time-varying attributes, leading to the cascading risks of security and trust. To circumvent these impediments, we develop an adaptive trust management based soft authentication and progressive authorization scheme by intelligently exploiting the time-varying communication link-related attribute of the transmitter to improve wireless security. First of all, the trust relationship between the transmitter and receiver is established based on the evaluation of selected physical layer attribute for fast authentication and multiple-level authorization. Through the designed trust model, the transmitter is authorized by the specific level of services/resources corresponding to its trust level, so that soft security is achieved. To dynamically update the trust level of the transmitter, we propose an online conformal prediction-based adaptive trust adjustment algorithm relying on the real-time validation of its attribute estimates at the receiver, thus resulting in progressive authorization. The performance of our scheme is theoretically analyzed in terms of its individual risk and individual satisfaction. Our simulation results demonstrate that the proposed scheme significantly improves the security performance and robustness in time-varying environments, and performs better than the static binary authentication scheme and existing physical layer authentication benchmarker.

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

In this treatise, we derive tractable closed-form expressions for the outage probability of the single cell multi-carrier non-orthogonal multiple access (MC-NOMA) downlink, where the transmitter side only has statistical CSI knowledge. In particular, we analyze the outage probability with respect to the total data rates (summed over all subcarriers), given a minimum target rate for the individual users. The calculation of outage probability for the distant user is challenging, since the total rate expression is given by the sum of logarithmic functions of the ratio between two shifted exponential random variables, which are dependent. In order to derive the closed-form outage probability expressions both for two subcarriers and for a general case of multiple subcarriers, efficient approximations are proposed. The probability density function (PDF) of the product of shifted exponential distributions can be determined for the near user by the Mellin transform and the generalized upper incomplete Fox's H function. Based on this PDF, the corresponding outage probability is presented. Finally, the accuracy of our outage analysis is verified by simulation results.

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

Spectrally efficient frequency division multiplexing (SEFDM) relying on index modulation (IM) has emerged as a promising multicarrier technique. In this paper, we develop a joint channel estimation and equalization method based on factor graphs for SEFDM-IM signaling over frequency-selective fading channels. By approximating the interference in the frequency domain, we reformulate the problem to obey a linear state-space model and construct a multi-layer factor graph. To support a reconfigurable architecture, non-orthogonal demodulation is adopted and the colored noise encountered is approximated by a complex auto-regressive (CAR) model. For deriving a low-complexity parametric Gaussian message passing (GMP)-based method, we exploit an expectation propagation (EP)-based technique for approximating the discrete a posteriori distributions of the transmitted symbols in a Gaussian form. To further simplify the result, variational message passing (VMP) is applied to an equivalent soft node to obtain a Gaussian form. Moreover, we also derive the Cramér-Rao lower bound (CRLB) in closed-form. The overall complexity only grows linearly with the number of subcarriers and logarithmically with the length of the channel's memory. Compared to its Nyquist signaling based counterpart, SEFDM-IM signaling relying on the proposed algorithm exhibits up to 25% higher bandwidth efficiency without any bit error rate (BER) performance degradation.

Sparse Bayesian learning (SBL)-based approximately sparse channel estimation schemes are conceived for space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems relying on trellis-based encoding and decoding over the data subcarriers. First, a pilot-aided channel estimation scheme is developed employing the multiple response extension of SBL (MSBL) framework. Subsequently, a novel data-aided joint channel estimation and data decoding framework relying on optimal maximum likelihood sequence detection (MLSD) is intrinsically amalgamated with our powerful EM-based MSBL algorithm. Explicitly, an MSBL-based MIMO channel estimate is gleaned in the E-step followed by a novel modified path-metric-based Viterbi decoder in the M-step. Our theoretical analysis characterizes the performance of the proposed schemes in terms of the associated frame error rate (FER) upper bounds by explicitly considering the effect of estimation errors along with evaluating the product measure of the STTC under consideration. Finally, our simulation results are complemented by the Bayesian Cramér-Rao bound (BCRB), the associated complexity analysis and the performance of the proposed schemes for validating the theoretical bounds.

In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and decoding methods are developed in the framework of message passing on a factor graph. Both the Wiener process and the truncated discrete cosine transform (DCT) expansion model are considered for approximating the PHN variation. Then based on these a factor graph is constructed for explicitly representing the joint estimation and detection problem. Taking full account of the sparse and structured a priori information arriving from the soft-in soft-out (SISO) decoder of a turbo receiver, a modified generalized approximate message passing (GAMP) algorithm is invoked for decoupling the frequency-domain symbols. In the decoupling step, mean field (MF) approximation is employed for solving the unknown nonlinear transform matrix problem imposed by PHN. Furthermore, merged belief propagation and MF (BP-MF) methods amalgamated both with sequential and parallel message passing schedules are introduced and compared to the proposed GAMP based algorithms in terms of their bit error ratio (BER) vs. complexity. Our simulation results demonstrate the efficiency of the proposed algorithms in the presence of both perfect and imperfect channel state information.

This paper analyses the performance of filter bank multicarrier (FBMC) signaling in conjunction with offset quadrature amplitude modulation (OQAM) in multi-user (MU) massive multiple-input multiple-output (MIMO) systems. Initially, closed form expressions are derived for tight lower bounds corresponding to the achievable uplink sum-rates for FBMC-based single-cell MU massive MIMO systems relying on maximum ratio combining (MRC), zero forcing (ZF) and minimum mean square error (MMSE) receiver processing with/without perfect channel state information (CSI) at the base station (BS). This is achieved by exploiting the statistical properties of the intrinsic interference that is characteristic of FBMC systems. Analytical results are also developed for power scaling in the uplink of MU massive MIMO-FBMC systems. The above analysis of the achievable sum-rates and corresponding power scaling laws is subsequently extended to multi-cell scenarios considering both perfect as well as imperfect CSI, and the effect of pilot contamination. The delay-spread-induced performance erosion imposed on the linear processing aided BS receiver is numerically quantified by simulations. Numerical results are presented to demonstrate the close match between our analysis and simulations, and to illustrate and compare the performance of FBMC and traditional orthogonal frequency division multiplexing (OFDM)-based MU massive MIMO systems.

Semi-blind (SB) channel estimation is conceived for millimeter wave (mmWave) analog-beamforming (AB) and hybrid-beamforming (HB)-based multiple-input multiple-output (MIMO) systems, which also exploits the data symbols for improving the estimation accuracy. A novel aspect of the proposed framework is that it directly estimates the analog beamformer/ combiner weights without necessitating the estimation of the entire mmWave MIMO channel matrix. By involving powerful matrix perturbation theoretic techniques, a closed-form expression is derived for the mean-squared-error (MSE) of the mmWave-AB-SB algorithm. As a further novelty, our mmWave-HB-SB technique relies on the decomposition of the channel matrix as the product of a decorrelating and a unitary matrix. Subsequently, the former is estimated purely relying on the unknown data symbols, whereas the latter is estimated exclusively from the training vectors. A lower bound on the MSE of the proposed mmWave-HB-SB technique is derived using the constrained Cramér-Rao lower bound (CRLB) framework. Furthermore, the performance gain of our mmWave-HB-SB technique over the conventional purely training-based scheme is also quantified analytically. Our simulation results demonstrate the superiority of the techniques advocated over the existing solutions and also verify the accuracy of our analytical findings.

Hierarchical Bayesian Kalman filter (HBKF) based schemes are conceived for doubly-selective sparse channel estimation in orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Initially, a pilot based multiple measurement vector (MMV) model is formulated for estimating the OSTBC MIMO-OFDM channel. This is followed by the development of a low-complexity, online pilot-based HBKF (P-HBKF) scheme for tracking the sparse time-varying frequency-selective channel. The salient advantages of the proposed P-HBKF technique are that it requires significantly lower number of pilot subcarriers, while also exploiting the inherent sparsity of the wireless channel. Subsequently, data detection is also incorporated in the proposed framework, leading to the development of a procedure for joint sparse doubly-selective channel estimation and symbol detection. Recursive Bayesian Cramér-Rao bounds and closed form expressions are also obtained for the asymptotic mean square error (MSE) based on the solution of the Riccati equation for the KF for benchmarking the performance. Simulation results are presented for validating the theoretical bounds and for comparing the performance of the proposed and existing techniques.

This paper determines the optimum secondary user (SU) power allocation and ergodic multicast rate of point-to-multipoint communication in a cognitive radio network (CRN) in the presence of various quality of service (QoS) constraints for the primary users (PUs). Using tools from extreme value theory (EVT), it is first proved that the limiting distribution of the minimum of independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Weibull distribution, when the user signal and the interferer signals undergo independent and non-identically distributed (i.n.i.d.) κ-μ shadowed fading. Also, the rate of convergence of the actual minimum distribution to the Weibull distribution is derived. This limiting distribution is then used for determining the optimum transmit power of a secondary network in an underlay CRN subject to three different QoS constraints at the primary network in a generalized fading scenario. Furthermore, the optimum transmit power and the asymptotic ergodic multicast rate of SUs is analyzed for varying channel fading parameters.

Quantum secure direct communication (QSDC) is capable of direct confidential communications over a quantum channel, which is achieved by dispensing with the key agreement channel of the well-known quantum key distribution (QKD). However, to make QSDC a practical reality, we have to mitigate its reliance on quantum memory, its immediate communication interruption caused by eavesdropping and its low transmission reliability due to the heavy qubit losses. Hence a new QSDC protocol is proposed based on a sophisticated coded single-photon DL04 QSDC protocol to tackle the open challenges. In particular, quantum memory is dispensed with and a high-accuracy secrecy capacity estimate is derived for this protocol by conceiving dynamic joint encryption and error-control (JEEC) coding. We demonstrate that this quantum-memory-free DL04 QSDC (QMF-DL04 QSDC) protocol inches closer to the quantum channel's capacity and significantly improves the original DL04 QSDC's robustness. Moreover, a rate-compatible low-rate JEEC coding scheme is designed for the proposed framework, and the JEEC code advocated is shown to approach the secrecy capacity, despite tolerating an extremely high loss of qubits in the time-varying wiretap channel. Our simulations and experimental results demonstrate that the QMF-DL04 QSDC scheme significantly increases both the secure information rate and the communication distance of the original DL04 protocol.

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by γ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to M-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of M for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, M, and the number of users, K, the ESG of NOMA over OMA increases linearly with both M and K. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized S-RDSM scheme, which is characterized by our theoretical analysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker's complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.

The paper considers the problem of beamforming design for a multi-cell network of downlink users, who either harvest energy or decode information or do both by receiving signals from the multi-antenna base station (BS) within a time slot and over the same frequency band. Our previous contributions have showed that the time-fraction based energy and information transmission, under which first the energy is transferred within the initial fraction of time and then the information is transferred within the remaining fraction, is the most efficient design alternative both in terms of its practical implementation and network performance. However, at the time of writing, both energy and information beamforming has only been implemented for proper Gaussian signaling (PGS), which has limited the network's throughput. Although the network throughput could be improved in some specific scenarios by using non-orthogonal multi-access (NOMA), this may compromise the user secrecy. In order to circumvent the above implementations, we conceive improper Gaussian signaling (IGS) for information beamforming, which enables the network to substantially improve its throughput in any scenario without jeopardizing the user secrecy despite its low-complexity signal processing at the user end. A simpler subclass of IGS is also considered, which also outperforms NOMA PGS and works under any arbitrary scenario.

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

In this work, we investigate a full-duplex (FD) millimeter wave (mmWave) multicell system, where the BS of each cell receives signals from uplink (UL) users and transmits signals to downlink (DL) users at the same time, over the same frequency band. We maximize the sum rate lower bound of the FD multicell system by jointly optimizing the digital and analog beamforming matrices at the base station (BS) and the transmit power levels of the UL users under total transmit power constraints and unit-modulus constraints (due to the analog beamforming matrices), in the presence of imperfect channel state information (CSI). The problem under study is very challenging due to the highly non-convexity of the objective function and constraints. We transform this problem into an equivalent but more tractable form and propose a novel iterative algorithm based on the penalty dual decomposition (PDD) to solve it. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of the original problem. Moreover, we also extend our proposed algorithm to the structure of subarray. Simulation results validate the effectiveness of the proposed algorithm as compared with conventional nonrobust and half-duplex (HD) algorithms.

The new paradigm of wirelessly powered two-tier heterogeneous networks (HetNets) is considered in this paper. Specifically, the femtocell base station (FBS) is powered by a power beacon (PB) and transmits confidential information to a legitimate femtocell user (FU) in the presence of a potential eavesdropper (EVE) and a macro base station (MBS). In this scenario, we investigate the secrecy beamforming design under three different levels of FBS-EVE channel state information (CSI), namely, the perfect, imperfect and completely unknown FBS-EVE CSI. Firstly, given the perfect global CSI at the FBS, the PB energy covariance matrix, the FBS information covariance matrix and the time splitting factor are jointly optimized aiming for perfect secrecy rate maximization. Upon assuming the imperfect FBS-EVE CSI, the worst-case and outage-constrained SRM problems corresponding to deterministic and statistical CSI errors are investigated, respectively. Furthermore, considering the more realistic case of unknown FBS-EVE CSI, the artificial noise (AN) aided secrecy beamforming design is studied. Our analysis reveals that for all above cases both the optimal PB energy and FBS information secrecy beamformings are of rank-1. Moreover, for all considered cases of FBS-EVE CSI, the closed-form PB energy beamforming solutions are available when the cross-tier interference constraint is inactive. Numerical simulation results demonstrate the secrecy performance advantages of all proposed secrecy beamforming designs compared to the adopted baseline algorithms.

This paper proposes a novel spatial-modulated multicarrier sparse code-division multiple access (SM/MC-SCDMA) system for achieving massive connectivity in device-centric wireless communications. In our SM/MC-SCDMA system, the advantages of both MC signalling and SM are amalgamated to conceive a low-complexity transceiver. Sparse frequency-domain spreading is utilized to mitigate the peak-to-average power ratio (PAPR) of MC signalling, as well as to facilitate low-complexity detection using the message passing algorithm. We then analyze the single-user bit error rate performance of SM/MC-SCDMA systems communicating over frequency-selective fading channels. Furthermore, the performance of SM/MC-SCDMA systems is evaluated based on both Monte-Carlo simulations and analytical results. We demonstrate that our low-complexity SM/MC-SCDMA transceivers are capable of achieving near-maximum likelihood (ML) performance even when the normalized user-load is as high as two, hence constituting a variable solution to support massive connectivity in device-centric wireless systems.

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

The security of spatial modulation (SM) aided networks can always be improved by reducing the desired link's power at the cost of degrading its bit error ratio performance and assuming the power consumed to artificial noise (AN) projection (ANP). We formulate the joint optimization problem of maximizing the secrecy rate (Max-SR) over the transmit antenna selection and ANP in the context of secure SM-aided networks. In order to solve this problem, we provide a pair of solutions, namely joint and separate solutions. Specifically, an accurate approximation of the SR is used for reducing the computational complexity, and the optimal AN covariance matrix (ANCM) is found by convex optimization for any given active antenna group (AAG). Then, given a large set of AAGs, simulated annealing mechanism is invoked for optimizing the choice of AAG, where the corresponding ANCM is recomputed by this optimization method as well when the AAG changes. To further reduce the complexity of the above-mentioned joint optimization, a low-complexity two-stage separate optimization method is also proposed. Moreover, when the number of transmit antennas tends to infinity, the Max-SR problem becomes equivalent to that of maximizing the ratio of the desired user's signal-to-interference-plus-noise ratio to the eavesdropper's. Thus, our original problem reduces to a fractional programming problem and a significant computational complexity reduction can be achieved. Finally, our simulation results verify the efficiency of the proposed methods in terms of the SR performance attained.

The concept of massive spatial modulation (SM) assisted vertical bell labs space-time (V-BLAST) (SM-VBLAST) system [1] is proposed, where SM symbols (instead of conventional constellation symbols) are mapped onto the VBLAST structure. We show that the proposed SM-VBLAST is a promising massive multiple input multiple output (MIMO) candidate owing to its high throughput and low number of radio frequency (RF) chains used at the transmitter. For the generalized massive SM-VBLAST systems, we first derive both the upper bounds of the average bit error probability (ABEP) and the lower bounds of the ergodic capacity. Then, we develop an efficient error correction mechanism (ECM) assisted compressive sensing (CS) detector whose performance tends to achieve that of the maximum likelihood (ML) detector. Our simulations indicate that the proposed ECM-CS detector is suitable both for massive SM-MIMO based point-to-point and for uplink communications at the cost of a slightly higher complexity than that of the compressive sampling matching pursuit (CoSaMP) based detector in the high SNR region.

Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.

The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network's edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.

In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.

As a benefit of using highly directional beams in millimeter wave systems, the downlink inter-cell interference (ICI) imposed on the users can be avoided, provided that the beams of neighbor cells do not point towards the user. We exploit this by designing a protocol for network-coordinated time- domain beam scheduling. Specifically, every pair of two neighbor cells maintains a beam collision table for recording the beam pairs that may inflict ICI upon each other. Then, to avoid beam-collision, the two neighbor cells exchange the necessary information to avoid the simultaneous activation of two beams recorded in one pair. More explicitly, our protocol supports a distributed cell coordination method without requiring any information exchanged between the user and the base station, once the beam collision table has been established. Furthermore, our theoretical analysis and numerical simulations demonstrate that the proposed protocol is capable of efficiently mitigating the ICI between the adjacent cells and hence improves the overall network performance.

In this paper, non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) is investigated, where the users are grouped into multiple clusters. Exploiting conjugate beamforming, the bandwidth efficiency (BE) of the system is derived while the assumption that the users performing realistic successive interference cancellation (SIC) based on only the knowledge of channel statistics. The max-min fairness problem of maximizing the lowest user BE is investigated and an iterative bisection method is developed to determine the optimal solution to the max-min BE problem. Numerical results are presented for validating the proposed design's performance, and a mode switching scheme is conceived for selecting a specific Mode = {OMA, NOMA} that maximizes the system's BE.

Simultaneous wireless information and power transfer (SWIPT) has gained significant popularity in the recent past owing to its applications in a wide range of use cases. Although SWIPT has been fairly well investigated in the literature, the existing work has mainly focused on attaining the optimal rate energy (RE) tradeoff assuming Gaussian input alphabet. However, practical systems operate with finite input alphabets such as quadratic-amplitude modulation (QAM)/phase-shift keying. We characterize the attainable RE tradeoff in SWIPT systems employing finite input alphabet for transmission over parallel Gaussian channels of say orthogonal frequency-division multiplexing subcarriers or multiple-input multiple-output streams. Some of the key results in the literature that assume Gaussian input alphabet are shown to be special cases of our results. Furthermore, we provide insights into our results with the aid of graphical illustrations, which throw light on the optimal power allocation policy for various energy-harvesting constraints. Furthermore, we consider practically relevant time-sharing and power-splitting schemes operating with finite input alphabet and characterize their RE tradeoff. Their optimal solutions in the asymptotic regime are obtained, which serve as low-complexity solutions suitable for practical implementation. Our simulation studies have demonstrated that the Gaussian input assumption significantly over-estimates the attainable RE tradeoff, especially when the signal set employed is small. Furthermore, it is observed through numerical simulations that the proposed optimal power allocation performs significantly better than the power allocation based on the Gaussian input assumption. Specifically, as much as 30% rate improvement is observed when employing the classic 4-QAM signal set.

This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU). Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or Gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely, that of minimizing the transmission power of the cognitive base station and that of maximizing the total harvested energy of the SUs, respectively. The optimization problems are challenging to solve, mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a 1-D search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the Gaussian CSI error model is generally better than that using the bounded CSI error model.

In order to beneficially exploit the scarce wireless spectral resources, spectrum sharing between communication and radar systems has become a promising research topic. However, traditional network association strategies may not result in efficient hybrid communication and radar systems. We circumvent this problem by formulating a partially observable Markov decision processes (POMDP) aided network association scheme, where the radar user acts as the primary user (PU), while the cognitive communication user is the secondary user (SU). For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, the communication user is configured for adaptively selecting its underlay or overlay access mode. Moreover, a low-complexity near-optimal reinforcement learning algorithm is proposed for the co-design by considering both its complexity and feasibility. Finally, we quantify the performance of our proposed POMDP based network association scheme.

In order to meet the latency requirements of the ultra-reliable low latency communication (URLLC) mode of the third-generation partnership project's long term evolution (LTE) mobile communication standard, this paper proposes a novel turbo decoding algorithm that supports an arbitrarily high degree of parallel processing, facilitating significantly higher processing throughputs and substantially lower processing latencies than the state-of-the-art (SOTA) LTE turbo decoder. As in conventional turbo decoding algorithms, the proposed Arbitrarily Parallel Turbo Decoder (APTD) decomposes each frame of information bits into a sequence of windows, where the bits within different windows are processed simultaneously using forward and backward recursions in a serial manner. However, in contrast to conventional turbo decoding algorithms, the APTD does not require different windows to be composed of an identical number of bits, which allows the use of an arbitrary number of windows and hence an arbitrary degree of parallelism, when decoding information bits of an arbitrary frame length. Furthermore, conventional turbo decoding algorithms alternate between simultaneously processing the windows in the upper decoder and those in the lower decoder. By contrast, the APTD processes the odd-indexed windows in the upper decoder at the same time as the even-indexed windows in the lower decoder and alternates between this and the reversed arrangement, hence further improving the decoding throughput and latency. Furthermore, the APTD achieves a reduced hardware resource requirement by calculating the extrinsic information based only on the outputs of the forward recursions, rather than based on both the forward and backward recursions of conventional turbo decoding algorithms. We demonstrate that the proposed APTD achieves superior latency, throughput, and computational efficiency than the SOTA LTE turbo decoder at all frame lengths, but particularly at the short frame lengths that are typically used in URLLC approaches. For example, at a frame length of N = 504 bits, the proposed APTD achieves an FER of 10-5at the same Eb/N0as I = 8 iterations of a conventional turbo decoder but with a computational efficiency that is 6 times higher than that of the SOTA turbo decoder, while achieving a latency and throughput that are 0.7 and 1.4 times those of the SOTA decoder, respectively.

Two decades of full-diversity high-rate MIMO research has created perfect Space-Time Block Codes (STBCs), including the Golden code. However, the major stumbling block of their wide-spread employment is their limited energy-efficiency. On one hand, the superposition of their signals results in a high Peak-to-Average Power Ratio (PAPR). On the other hand, the total number of equivalent Inter-Antenna Interference (IAI) contributions that the receiver has to deal with is increased to IAI = M2upon using M Transmit Antennas (TAs), which is a substantial extra price compared to the IAI = M of V-BLAST. Against this background, we propose a new family of Finite-Cardinality Generalized Space-Time Shift Keying (FC-GSTSK). More explicitly, the proposed FC-GSTSK is capable of outperforming both V-BLAST and STBC, which is the ultimate objective of full-diversity high-rate MIMO design. Furthermore, following the index modulation philosophy, the proposed FC-GSTSK replaces the signal-additions by the data-carrying signal-selection process. As a benefit, the FC-GSTSK substantially reduces the PAPR of signal transmission. As a further advantage, the equivalent IAI imposed on signal detection is reduced back to the same level as that of the V-BLAST. Moreover, the proposed FC-GSTSK is even capable of consistently outperforming the perfect STBCs in terms of its Peak Signal to Noise-power Ratio (PSNR) that takes into account the power consumption at the transmitter. As a further advance, the reduced-RF-chain based version of FC-GSTSK is also capable of outperforming both Generalized Spatial Modulation (GSM) and Space-Time Block Coded Spatial Modulation (STBC-SM) without increasing the PAPR and the equivalent IAI.

Secure wireless information and power transfer based on directional modulation is conceived for amplify-and-forward relaying networks. Explicitly, we first formulate a secrecy rate maximization (SRM) problem, which can be decomposed into a twin-level optimization problem and solved by a one-dimensional (1D) search and semidefinite relaxation (SDR) technique. Subsequently, in order to reduce the search complexity, we formulate an optimization problem based on maximizing the signal-to-leakage-AN-noise-ratio (Max-SLANR) criterion, and transform it into a SDR problem. In addition, the relaxation is proved to be tight according to the classic Karush-Kuhn-Tucker (KKT) conditions. Finally, to reduce the computational complexity, a successive convex approximation (SCA) scheme is proposed to find a near-optimal solution. The complexity of the SCA scheme is much lower than that of the SRM and the Max-SLANR schemes. Simulation results demonstrate that the performance of the SCA scheme is very close to that of the SRM scheme in terms of its secrecy rate and bit error rate, but much better than that of the zero forcing scheme.

The birth of wireless communication systems nearly a century ago has transformed and redefined the way humans communicate and interact. This transformation has evolved over many years and has brought along not only seamless connectivity for human interactions but also communication between machines and devices. While these communication systems are manmade artifacts, the research community has more recently turned its attention to other communication strategies that have spontaneously evolved in nature.

The engineering vision of relying on the “smart sky” for supporting air traffic and the “internet-above-the-clouds” for in-flight entertainment has become imperative for the future aircraft industry. Aeronautical ad hoc networking (AANET) constitutes a compelling concept for providing broadband communications above clouds by extending the coverage of air-to-ground (A2G) networks to oceanic and remote airspace via autonomous and self-configured wireless networking among commercial passenger airplanes. The AANET concept may be viewed as a new member of the family of mobile ad hoc networks (MANETs) in action above the clouds. However, AANETs have more dynamic topologies, larger and more variable geographical network size, stricter security requirements, and more hostile transmission conditions. These specific characteristics lead to more grave challenges in aircraft mobility modeling, aeronautical channel modeling, and interference mitigation as well as in network scheduling and routing. This paper provides an overview of AANET solutions by characterizing the associated scenarios, requirements, and challenges. Explicitly, the research addressing the key techniques of AANETs, such as their mobility models, network scheduling and routing, security, and interference, is reviewed. Furthermore, we also identify the remaining challenges associated with developing AANETs and present their prospective solutions as well as open issues. The design framework of AANETs and the key technical issues are investigated along with some recent research results. Furthermore, a range of performance metrics optimized in designing AANETs and a number of representative multiobjective optimization algorithms are outlined.

Hybrid precoding is essential for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems due to its inherent advantage of a high gain, whilst alleviating the high cost of hardware. However, most of the existing literature considered either the narrowband or wideband single-user mmWave MIMO scenarios. Hence in this paper we focus our attention on the more challenging design of hybrid Transmit Precoding (TPC) for wideband multiuser mmWave MIMO systems by exploiting the long-term channel's covariance matrix and the angle of departure (AoD) information. Specifically, in the analog TPC designed, firstly, the analog TPC matrix having an infinite angular resolution is constructed based on the channel's covariance matrix. Then, we also propose a non-uniformly spaced quantization codebook based analog TPC having finite angular resolution. Furthermore, a phase compensation operation is carried out to alleviate the effect of beam squint. As for the design of the digital TPC, a two-stage scheme is proposed to cancel the inter-user interference and to attain multiplexing gains. We study the effects of various parameters on the achievable sum rate and demonstrate with the aid of our simulation results that the proposed hybrid TPC is capable of achieving a similar performance to the excessive-complexity fully digital TPC.

We study the physical-layer security of a cognitive radio system in the face of multiple eavesdroppers (EDs), which is composed of a secondary base station (SBS), multiple secondary users (SUs) as well as a pair of primary transmitter (PT) and primary receiver (PR), where the SUs first harvest energy from their received radio frequency signals transmitted by the PT and then communicate with the SBS relying on opportunistic scheduling. We consider two specific user scheduling schemes, namely, the channel-aware user scheduling (CaUS) and the energy-aware user scheduling (EaUS). In the CaUS scheme, an SU having the best instantaneous SU-SBS link (spanning from SUs to SBS) will be activated to communicate with the SBS. By contrast, the EaUS scheme takes into account both the amount of energy harvested from the PT and the instantaneous quality of the SU-SBS link. We analyze the security-reliability tradeoff (SRT) of both the CaUS and EaUS schemes in terms of their intercept versus outage probability. We also provide the SRT analysis of traditional round-robin user scheduling (RrUS) used as a benchmarker of the CaUS and EaUS schemes. We demonstrate that the EaUS scheme achieves the best outage and secrecy performance in the high main-to-eavesdropper ratio (MER) region, but a worse secrecy performance than the CaUS method in the low-MER region. Moreover, from a security versus reliability perspective, the CaUS outperforms both the EaUS and the RrUS in the low-MER region. Surprisingly, this also implies that although the user scheduling criterion of EaUS exploits the knowledge of both the amount of harvested power and instantaneous channel state information (CSI), it exhibits a degraded physical-layer security in the low-MER region due to the fact that the increased harvested energy is beneficial not only for the legitimate SBS receiver but also for the EDs.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

The maturing massive multiple-input multiple-output (MIMO) literature has provided asymptotic limits for the rate and energy efficiency (EE) of maximal ratio combining/ maximal ratio transmission (MRC-MRT) relaying on two-way relays (TWRs) using the amplify-and-forward (AF) principle. Most of these studies consider time-division duplexing and a fixed number of users. To fill the gap in the literature, we analyze the MRC-MRT precoder performance of an N-antenna AF massive MIMO TWR, which operates in a frequency-division duplex mode to enable two-way communication between 2M = [Nα] single-antenna users, with α ∈ [0, 1), divided equally into two groups of M users. We assume that the relay has realistic imperfect uplink channel state information (CSI), and that quantized downlink CSI is fed back by the users relying on B ≥ 1 bits per-user per relay antenna. We prove that for such a system with α ∈ [0, 1), the MRC-MRT precoder asymptotically cancels the multi-user interference (MUI) when the supremum and infimum of large-scale fading parameters are strictly nonzero and finite, respectively. Furthermore, its per-user pairwise error probability converges to that of an equivalent AWGN channel, as both N and the number of users 2M = [Nα] tend to infinity, with a relay power scaling of Pr= (2MEr/N) and Erbeing a constant. We also derive upper bounds for both the per-user rate and EE. We analytically show that the quantized MRC-MRT precoder requires as few as B = 2 bits to yield a BER, EE, and per-user rate close to the respective unquantized counterparts. Finally, we show that the analysis developed herein to derive a bound on α for MUI cancellation is applicable both to Gaussian as well as to any arbitrary non-Gaussian complex channels.

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machinelearning as an intelligent process to learn and utilize the complex time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single-dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least mean square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

Small-cell aided ultra-dense networks (UDNs) constitute an efficient solution to the ever-increasing thirst for more data. Thanks to the vast untapped high-frequency spectrum of visible light, visible light communications (VLCs) are a natural candidate for UDN. In this paper, layered asymmetrically clipped optical OFDM (LACO-OFDM) aided ultra-dense VLC (UD-VLC) is investigated in terms of its user association, multiple access (MA), and resource allocation. To handle the severe inter-cell interference (ICI) amongst the densely deployed access points, we propose a novel overlapped clustering technique relying on a hybrid non-orthogonal MA and orthogonal MA scheme for enhancing the performance, with the aid of our dynamic resource allocation conceived. Our simulations show that the proposed LACO-OFDM aided UD-VLC using our hybrid MA scheme is more robust against the ICI, at a price of modestly decreasing the sum throughput.

A new joint best relay and jammer selection (JBRJS) scheme is conceived for enhancing the physical layer security (PLS) of cooperative networks relying on multiple energy harvesting (EH) aided intermediate nodes, which accumulate energy based on the power splitting (PS) protocol. Specifically, we select the best intermediate node as the relay, whilst exploiting all the remaining nodes as friendly jammers. Furthermore, we investigate the joint optimization of the PS ratio and the relay-aided beamforming for maximizing the system's secrecy rate and present a full channel state information (CSI) based joint PS and beamforming (fJPSB) scheme as the optimal solution by converting the optimization problem formulated into a singlevariable optimization problem. We also propose a partial-CSI based JPSB (pJPSB) method for the scenario where only the main link's CSI is available. Our numerical results show that the proposed JBRJS scheme beneficially enhances the PLS compared to the joint random relay and jammer selection (JRRJS) and to the pure best relay selection (PBRS) schemes. Moreover, the secrecy rate of the proposed fJPSB and pJPSB schemes is obviously higher than that of the fixed PS and beamforming (FPSB) method, and it further increases with the number of the relay's transmit antennas.

In this paper, we conceive a compressed sensing (CS)-aided multi-dimensional index modulation (IM) scheme, where the benefits of space-time shift keying, orthogonal frequency-division multiplexing relying on the frequency domain IM, and spatial modulation are explored. Explicitly, extra information bits are transmitted through the active indices of both the transmit antennas and subcarriers, while striking a flexible design tradeoff between the throughput and the diversity order. Furthermore, CS is invoked in both the transmitter and the receiver of our multi-dimensional system for the sake of improving the system's design flexibility, while reducing the detector's complexity. We first present the maximum likelihood (ML) detector of the proposed CS-aided multi-dimensional IM system for characterizing the best-case bound of the proposed system's performance. Specifically, an upper bound is derived for the average bit error probability, and it is observed that the derived theoretical upper bound becomes very tight with the ML detector simulation curves as the signal-to-noise ratio increases. Then, we propose a reduced complexity detector imposing only a modest bit-error-ratio degradation, where we analyze the computational complexities of both the ML detector and the reduced complexity detector. Furthermore, a soft-input soft-output decoder is proposed for attaining a near-capacity performance, which is analyzed with the aid of extrinsic information transfer (EXIT) charts. The maximum achievable rate of the proposed CS-aided multi-dimensional IM system relying both on the ML detection and on our reduced-complexity-based detector is also evaluated using EXIT charts. In addition, the discrete-input continuous-output memoryless channel capacity of the proposed CS-aided multi-dimensional IM scheme is formulated.

Joint optimal resource allocation and probabilistic caching design is conceived for device-to-device (D2D) communications in a heterogeneous wireless network (HetNet) relying on full-duplex (FD) relays. In particular, popular contents can be cached at user devices and at relays that are located close to users. A user may request the contents of interest from another user via D2D communications and also from a nearby relay equipped with FD radios. If the requested contents are not found in the buffers of other users/relays within the coverage range, users may opt for connecting to the macro base station (MBS) via a relay by using an FD communication. Furthermore, we propose a beneficial mobility-aware coded caching philosophy for D2D communications in the HetNet considered. Especially, we model the mobility pattern of users as discrete random jumps and exploit coded caching for improving the throughput attained. Subsequently, we develop mathematical models for analyzing the throughput in the presence of edge caching, where both the system-level co-channel interference and the FD self-interference are considered. We circumvent the high complexity of stochastic optimization by developing low-complexity optimization. Finally, numerical results are presented to illustrate the theoretical findings developed in this paper and quantify the throughput gains attained.

A novel multiple-input and multiple-output (MIMO) transmission scheme termed as generalized quadrature spatial modulation (G-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and spatial multiplexing for the sake of achieving a high throughput, despite relying on a low number of radio frequency (RF) chains. In the proposed G-QSM scheme, the conventional constellation points of the spatial multiplexing structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by the classic amplitude/phase modulated (APM) constellation points. The upper bounds of the average bit error probability (ABEP) of the proposed G-QSM system in high throughput massive MIMO configurations are derived. Furthermore, an efficient multipath orthogonal matching pursuit (EM-OMP)-based compressive sensing (CS) detector is developed for our proposed G-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes in massive MIMO configurations.

In this treatise, first of all, we conceive a generic multiple-symbol differential sphere detection (MSDSD) solution for both single- and multiple-antenna-based noncoherent schemes in both uncoded and coded scenarios, where the high-mobility aeronautical Ricean fading features are taken into account. The bespoke design is the first MSDSD solution in the open literature that is applicable to the generic differential space-time modulation (DSTM) for transmission over Ricean fading. In the light of this development, the recently developed differential spatial modulation and its diversity counterpart of differential space-time block coding using index shift keying are specifically recommended for aeronautical applications owing to their low-complexity single-RF and finite-cardinality features. Moreover, we further devise a noncoherent decision-feedback differential detection and a channel-state information estimation aided coherent detection, which also take into account the same Ricean features. Finally, the advantages of the proposed techniques in different scenarios lead us to propose for the aeronautical systems to adaptively: 1) switch between coherent and non-coherent schemes; 2) switch between single- and multiple-antenna-based schemes as well as; and 3) switch between high-diversity and high-throughput DSTM schemes.

The matrix-based differential encoding invoked by Differential Space-Time Modulation (DSTM) typically results in an infinite-cardinality of arbitrary signals, despite the fact that the transmit antennas (TAs) can only radiate a limited number of patterns. As a remedy, the recently developed differential spatial modulation (DSM) is capable of avoiding this problem by conceiving a beneficial sparse signal matrix design, which also facilitates low-complexity single-RF signal transmission. Inspired by this development, the differential space-time block code using index shift keying (DSTBC-ISK) further introduces a beneficial diversity gain without compromising the DSM's appealingly low transceiver complexity. However, the DSTBC-ISK's performance advantage tends to diminish as the throughput increases, especially when an increased number of Receive Antennas (RAs) is used. By contrast, the classic Differential Group Code (DGC) that actively maximizes its diversity gain for different multiple-input-multiple-output (MIMO) system setups is capable of achieving a superior performance, but its detection complexity grows exponentially with the throughput. Against this background, we propose the differential space-time shift keying using Diagonal Algebraic Space-Time scheme, which is the first DSTM that is capable of achieving the DGC's superior diversity gain at high throughputs without compromising the DSM's low transceiver complexity. As a further advance, we also conceive a new differential space-time shift keying using Threaded Algebraic Space-Time arrangement, which is capable of achieving an even further improved diversity gain at a substantially reduced signal detection complexity compared to the best DGCs. Furthermore, in order to strike a practical tradeoff, we develop a generic multi-element and multi-level-ring Amplitude Phase Shift Keying design, and we also arrange for multiple reduced-size DSTM sub-blocks to be transmitted in a permuted manner, which exhibits an improved diversity-throughput tradeoff.

Both wireless small-cell communications and software-defined networking (SDN) in wired systems continue to evolve rapidly, aiming for improving the quality of experience (QoE) of users. Against this emerging landscape, we conceive scalable video streaming over SDN-aided dense smell-cell networks by jointly optimizing the video layer selection, the wireless resource allocation, and the dynamic routing of video streams. In the light of this ambitious objective, we conceive a dense software-defined small-cell network architecture for the fine-grained manipulation of the video streams relying on the cooperation of small-cell base stations. Based on this framework, we formulate the scalable video streaming problem as maximizing the time-averaged QoE subject to a specific time-averaged rate constraint as well as to a resource constraint. By employing the classic Lyapunov optimization method, the problem is further decomposed into the twin sub-problems of video layer selection and wireless resource allocation. Via solving these sub-problems, we derive a video layer selection strategy and a wireless resource allocation algorithm. Furthermore, we propose a beneficial routing policy for scalable video streams with the aid of the so-called segment routing technique in the context of SDN, which additionally exploits the collaboration of small-cell base stations. Our results demonstrate compelling performance improvements compared with the classic PID control theory-based method.

Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.

This paper considers a passive localization scenario relying on a single transmitter, several receivers, and multiple moving targets to be located. The so-called “passive” targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation-maximization-based passive localization method for asynchronous receivers (EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cramér-Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cramér-Rao bound and significantly improve the localization performance compared with the conventional methods.

Multi-class channel coded layered asymmetrically clipped optical orthogonal frequency-division multiplexing (LACO-OFDM) is proposed, where the achievable rate of the system is derived based on our mutual information analysis. We conceive a multi-class channel encoding scheme integrated with the layered transmitter. At the receiver, both the coded and uncoded likelihood ratios are extracted for inter-layer interference cancellation and symbol detection, respectively. Simulations are conducted, and the results show that our design approaches the achievable rate within 1.1 dB for 16-QAM fourlayer LACO-OFDM with the aid of a half-rate eight-iteration turbo code at BER = 10-3, outperforming its conventional counterpart by about 3.6 dB.

Index modulation (IM) is an attractive concept for next generation communication systems. However, as an emerging technique, there are still some challenges need to be tackled in this frontier. In this paper, we consider two aspects of IM, which are diversity and detection complexity. Specifically, we propose a linear precoding assisted index modulation (LPIM) scheme for orthogonal frequency division multiplexing (OFDM) systems. We commence by analyzing the diversity and coding gains of the proposed scheme. Moreover, a detailed codebook design criterion is proposed. Then, our LPIM codebook is designed based on the maximum diversity and coding gain criteria. In contrast to the signaling model of the existing full diversity precoder designed for OFDM, we introduce a modeling method to link the zero-valued IM symbols to the origin of a Lattice for implementing our full diversity precoder designed for OFDM-IM. Both analytical and computer simulation results are provided for characterizing the attainable performance of OFDM-LPIM, demonstrating that it is capable of achieving full diversity as well as an attractive coding gain. However, the maximum-likelihood (ML) detection complexity of OFDM-LPIM is excessive, hence a low-complexity generalized iterative residual check detector (GIRCD) is proposed, which is inspired by the existing sparse recovery algorithms. Finally, computer simulation results are provided for demonstrating that GIRCD can provide a beneficial trade-off between bit error ratio performance and complexity.

Single-carrier frequency-domain turbo equalization (SC-FDTE) has gained widespread adoption in the emerging broadband spatial modulation (SM) systems operating in frequency-selective channels, where the channel model considered is a quasi-static Rayleigh fading channel. In this paper, a new class of robust FDTE designs based on the minimum mean-square error (MMSE) criterion is conceived for broadband single-carrier SM (SC-SM) systems relying on realistic imperfect channel knowledge. First, a robust time-domain soft-decision feedback (TDSDF)-aided FDTE is proposed to cope with channel estimation errors at the receiver. Furthermore, its robust frequency-domain soft-decision feedback (FDSDF)-aided counterpart is derived to offer a low-complexity approximate solution. Finally, by exploiting the carefully selected reliable soft-decision output of the channel decoder as pilots, we refine the resultant decision-directed channel estimation. As a benefit, the performance of the two robust FDTEs can be further improved. Both our simulation results and our extrinsic information transfer (EXIT) chart analysis demonstrate that the proposed robust FDTEs achieve significant performance improvements over the conventional FDTEs.

In this paper, we investigate the physical layer security of a full-duplex base station (BS)-aided system in the worst case, where an uplink transmitter (UT) and a downlink receiver (DR) are equipped with a single antenna, while a powerful eavesdropper is equipped with multiple antennas. For securing the confidentiality of signals transmitted from the BS and UT, an artificial noise (AN)-aided secrecy beamforming scheme is proposed, which is robust to the realistic imperfect state information of both the eavesdropping channel and the residual self-interference channel. Our objective function is that of maximizing the worst-case sum secrecy rate achieved by the BS and UT, through jointly optimizing the beamforming vector of the confidential signals and the transmit covariance matrix of the AN. However, the resulting optimization problem is non-convex and non-linear. In order to efficiently obtain the solution, we transform the non-convex problem into a sequence of convex problems by adopting the block coordinate descent algorithm. We invoke a linear matrix inequality for finding its Karush-Kuhn-Tucker (KKT) solution. In order to evaluate the achievable performance, the worst-case secrecy rate is analytically derived. Furthermore, we construct another secrecy transmission scheme using the projection matrix theory for performance comparison. Our simulation results show that the proposed robust secrecy transmission scheme achieves substantial secrecy performance gains, which verifies the efficiency of the proposed method.

The joint design of hybrid beamforming matrices is conceived for multiuser mm-wave full-duplex (FD) multiple-input multiple-output (MIMO) relay-aided systems in the presence of realistic channel state information (CSI) errors. Specifically, considering a probabilistic CSI error model, we maximize the system's worst-case sum rate by jointly optimizing the base station's (BS's) analog and digital beamforming matrices, plus the analog receive and transmit beamforming matrices of the relay station (RS) as well as its digital amplify-and-forward beamforming matrix under practical constraints. Explicitly, the transmit power constraints of the BS and RS, the residual self-interference power constraint of the RS, the per-user quality of service constraints, and the unit-modulus constraints on the analog beamforming matrix elements are all taken into account. Since the resultant optimization problem is very challenging due to its highly nonlinear objective function and nonconvex coupling constraints, we first transform it into a more tractable form. We then develop a novel joint optimization algorithm based on the penalty dual decomposition (PDD) technique to solve the resultant problem. The proposed PDD-based algorithm performs double-loop iterations: the inner loop updates the optimization variables in a block coordinate descent fashion, while the outer loop adjusts the Lagrange multipliers and penalty parameter, hence ensuring convergence to the set of stationary solutions of the original problem. Our simulations show that the mm-wave FD hybrid MIMO relay systems relying on our new algorithm significantly outperform both their non-robust FD and conventional half-duplex counterparts.

We investigate the energy efficiency (EE) of multiple-input-multiple-output (MIMO) amplify-and-forward relaying networks relying on the realistic imperfect channel state information (CSI). Specifically, the relay jointly optimizes the source covariance and relay beamforming matrices by maximizing the EE under additive or multiplicative relay-destination CSI errors. The optimal channel-diagonalizing structure is derived for the source covariance and relay beamforming matrices under the spectral-norm constrained additive or multiplicative CSI error. Then, the existence of a saddle point is proved, which shows that the channel-diagonalizing transmission strategy is optimal in the robust EE maximization under these two types of CSI errors, and the original matrix-valued fractional robust EE problem is transformed into a scalar fractional problem. We propose the Dinkelbach method-based alternating optimization scheme for this transformed robust EE problem, which is capable of finding a locally optimal solution of the original robust EE problem efficiently, and show that the semi-closed-form solution to each of the two associated subproblems can be obtained. We then prove that the channel-diagonalizing transmission strategy remains optimal when the statistically imperfect source-relay channel is additionally imposed. We also extend our work into multi-hop MIMO relaying scenarios and prove that the channel-diagonalizing structure is optimal for the source covariance matrix and the multiple relay beamforming matrices.

The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users' rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

The weighted sum-rate maximization problem of ultra-dense cloud radio access networks is considered. The user-centric clustering is adopted for reducing the complexity. To reduce the training overhead, one only needs to estimate the intra-cluster channel-state information (CSI), while only the large-scale channel gains are available outside the cluster. We first derive the rate lower bound (LB) relying on Jensen's inequality. For the special case of non-overlapping clusters, the accurate data rate expression is derived in the closed form. The simulation results show the tightness of the LB for both the overlapped and non-overlapped cases. Then, we consider an alternative problem where the actual data rate is replaced by its LB, which constitutes a non-convex optimization problem. First, the globally optimal solution is obtained by applying the high-complexity outer polyblock approximation (OPA) algorithm. Then, we invoke the reduced-complexity modified weighted minimum mean square error (WMMSE) algorithm for mitigating the deleterious effects of the realistic imperfect CSI. For the subproblem solved by each WMMSE iteration, the beamforming vectors are derived in the closed form relying on the Lagrangian dual decomposition method. Finally, our simulation results show that the modified WMMSE algorithm's performance is comparable to that of the high-complexity OPA algorithm, which outperforms other benchmark algorithms.

This paper investigates how to enhance the energy and spectral efficiency (ESE) performance of large-scale cellular networks by offloading mobile traffic with the aid of device-to-device (D2D) communication. By appropriately exploiting the D2D-based mobile-traffic offloading mechanism, the users' behaviors and the specific network operating conditions, we develop an ESE evaluation framework for large-scale D2D-enabled cellular networks. This framework enables us to characterize the explicit relationship between the network's ESE and the offloading parameters as well as to quantify the influence of the users' behavior. Explicitly, we quantify the effects of the mobile-traffic intensity, the users' quality of service requirements as well as the base station density and other cellular system parameters on the achievable ESE. Tractable closed-form ESE-expressions are derived for a pair of spectrum sharing schemes, namely, D2D overlay and underlay in-band modes. Furthermore, we apply the analytical results to derive an optimal D2D-enabled mobile-traffic offloading scheme for the D2D overlay cellular networks to maximize the network's ESE under a specific maximal cellular user outage and D2D transmitter power constraint. The numerical and simulation results are provided to verify our modeling accuracy and to demonstrate the impact of the system parameters on the achievable ESE.

Beamspace channel estimation is essential for wideband millimeter-wave (mmWave) MIMO with lens antenna array to achieve substantial increase in data rates with considerably reduced number of radio-frequency chains. However, most of existing beamspace channel estimation schemes are designed for narrowband mmWave systems, while rather scarce wideband schemes ideally assume beamspace channel enjoys the common support in frequency-domain. In this paper, inspired by the classical successive interference cancellation for multi-user detection, we propose an efficient successive support detection (SSD) based scheme without the assumption of common support. Specifically, we first demonstrate that each path component of the wideband beamspace channel exhibits a unique frequency-varying sparse structure. Based on this, we then successively estimate all sparse path components. For each path component, its supports at different frequencies are jointly estimated to improve the estimation accuracy, and then its influence is removed to estimate the remained path components. Once all path components have been estimated, the wideband beamspace channel can be recovered at a low complexity. Simulation results verify that the proposed SSD based beamspace channel estimation scheme achieves higher accuracy than existing wideband schemes.

In order to beneficially exploit wireless spectral resources, spectrum sharing between communication systems and radar systems has become a popular research topic. However, traditional network association strategies may not result in an efficient co-system. We circumvent this problem by formulating a partially observable Markov decision process (POMDP) aided network association scheme. For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, communication users are capable of adaptively selecting underlay or overlay access mode. Moreover, a near-optimal reinforcement learning algorithm is proposed considering both the computational complexity and feasibility. Finally, simulations are conducted in order to evaluate the effectiveness of our proposed POMDP based network association scheme.

Space-time shift keying (STSK) constitutes a beneficial multiple-input-multiple-output (MIMO) technique that strikes a compelling trade-off between the multiplexing and diversity gains attained. Its close relative, namely layered multi-group STSK (LMG-STSK) combines the concepts of multi-user MIMO, STSK and beamforming for simultaneously conveying information to multiple users. Generalized frequency division multiplexing (GFDM) has been proposed as a promising multicarrier modulation candidate for dispersive wideband channels since it benefits from its time-frequency domain structure when combined with a powerful MIMO scheme. As a further evolution, we propose a novel GFDM-aided adaptive layered multi-group STSK (LMG-STSK) system for the downlink of millimeter wave communications systems. The proposed system tackles the propagation challenges of the high pathloss at millimeter wave frequencies, where multiple users are clustered into groups served by different antenna layers at the transmitter. We propose an adaptive scheme that can adapt both the number of transmit antennas (TAs) per layer based on the number of users in each user group in order to maximize the number of users served and also the STSK codeword in order to maximise the achievable user throughput.

Millimeter wave (mmWave) communications has gained considerable attention due to the availability of large bandwidths, which can be harnessed to meet the ever-increasing data rate demands. Directional beamforming combined with baseband precoding should be used owing to the high propagation losses encountered at mmWave frequencies. This is typically referred to as hybrid beamforming. In hybrid beamforming arrangements, the adjacent antenna elements are closely spaced, typically at half-wavelength spacing in order to compensate for the propagation losses. In this antenna array configuration, the mutual coupling between the adjacent antenna elements becomes significant and may limit the performance of the system. Therefore, in this paper, we propose a reduced-complexity near-optimal detection scheme, namely the so-called Element-based Lattice Reduction algorithm, for hybrid beamforming in mmWave communications and we investigate its performance in the presence of mutual coupling. We demonstrate that the mutual coupling affects the spatial correlation of the channels between the different antennas depending on the distance between the antenna elements, which has a direct effect on the achievable rate as well as bit error ratio (BER) performance of the system.

The Internet of Things (IoT) will support a massive number of devices, which will be connected to the wireless network. In the wireless IoT, the base station serves a wide variety of devices in the same time-frequency resource, where it is expected that the number of devices will be greater than the number of base station antennas. This results in a rank-deficient system. In this paper, we propose a minimum bit error ratio (MBER) precoder for rank-deficient MIMO systems in the context of the IoT, where the IoT devices are generally stationary. We invoke the particle swarm optimization (PSO) algorithm for solving the non-linearly constrained MBER problem and we show that the PSO assisted MBER precoder outperforms the conventional zero forcing and linear minimum mean squared error (LMMSE) precoders, which produce an error floor in these challenging rank-deficient scenarios.

With an unprecedented amount of sensitive private data generated by mobile user equipment (UE), securing the emerging ultra-dense networks (UDNs) becomes critical. Although involving more access points (APs) is potentially capable of enhancing both the UE's throughput and security, the energy consumption becomes significant. In this paper, we investigate secure UDNs in the context of the user-centric clustering of UDNs from a secrecy energy efficiency perspective, while satisfying both the throughput and the security of each UE. We first propose a secure user-centric clustering architecture by introducing both a dedicated jamming strategy and an embedded jamming strategy, both of which degrade the overheard signals of the eavesdroppers and guarantee secure transmission relying on the different APs' involvement status. We formulate the secure user-centric clustering design for both known and unknown eavesdropper channel state information (CSI), whilst maximizing the secrecy energy efficiency with the aid of various secure transmission schemes. Since the problem formulated is a non-convex mixed integer non-linear programming problem, we develop a set of heuristic greedy secure user-centric clustering algorithms for diverse operating scenarios. Finally, our numerical results reveal the quantitative benefits of the proposed secure user-centric clustering architectures as a function of the network densities (i.e., AP, UE, and eavesdropper) and of both the throughput and the security constraints on the secrecy energy efficiency trade-off in different scenarios.

The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit beamforming is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase synchronization is required. By exploiting the classic successive interference cancellation technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1-norm technique to approximate the l0-norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the coherent one in the low fronthaul capacity regime, which is the case in ultradense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed.

We propose a regularized zero-forcing transmit precoding (RZF-TPC)-aided and distance-based adaptive coding and modulation (ACM) scheme to support aeronautical communication applications, by exploiting the high spectral efficiency of the large-scale antenna arrays and link adaption. Our RZF-TPC-aided and distance-based ACM scheme switches its mode according to the distance between the communicating aircraft. We derive the closed-form asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the RZF-TPC for the aeronautical channel, which is Rician, relying on a non-centered channel matrix that is dominated by the deterministic line-of-sight component. The effects of both realistic channel estimation errors and of the co-channel interference are considered in the derivation of this approximate closed-form SINR formula. Furthermore, we derive the analytical expression of the optimal regularization parameter that minimizes the mean square detection error. The achievable throughput expression based on our asymptotic approximate SINR formula is then utilized as the design metric for the proposed RZF-TPC-aided and distance-based ACM scheme. Monte-Carlo simulation results are presented for validating our theoretical analysis as well as for investigating the impact of the key system parameters. The simulation results closely match the theoretical results. In the specific example that two communicating aircrafts fly at a typical cruising speed of 920km/h, heading in opposite direction over the distance up to 740km taking a period of about 24 min, the RZF-TPC-aided and distance-based ACM is capable of transmitting a total of 77 GB of data with the aid of 64 transmit antennas and four receive antennas, which is significantly higher than that of our previous eigen-beamforming transmit precoding-aided and distance-based ACM benchmark.

Visible light communication (VLC) is considered as a promising candidate to improve the performance of indoor communication as the complement of wireless radio frequency (RF) communications due to the scarcity of RF resources. Combining the VLC with software-defined small-cell networks will substantially improve the user data rates in indoor heterogeneous networks. In this paper, we introduce the software-defined philosophy into orthogonal frequency-division multiple access-based heterogeneous software-defined and twinned VLC and RF small-cell networks. The pivotal issues of energy efficient (EE) subchannel and power allocation are investigated in the context of software-defined VLC and RF small-cell networks. We formulate the EE resource allocation problem as a non-convex optimization problem, and then, transform it into a convex one using Dinkelbach's method. In addition, distributed subchannel and power allocation algorithms for both VLC and RF are proposed for solving the problem based on the powerful alternative direction method of multipliers. Simulation results verify the effectiveness of resource allocation algorithms conceived for the heterogeneous software-defined twinned VLC and RF small-cell networks in terms of its good convergence and overall performance.

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.

Wireless multihop networks (WMHNs) have to strike a trade-off among diverse and often conflicting quality-of-service requirements. The resultant solutions may be included by the Pareto front under the concept of Pareto optimality. However, the problem of finding all the Pareto-optimal routes in WMHNs is classified as non-deterministic polynomial-hard, since the number of legitimate routes increases exponentially, as the nodes proliferate. Quantum computing offers an attractive framework of rendering the Pareto-optimal routing problem tractable. In this context, a pair of quantum-assisted algorithms has been proposed, namely the non-dominated quantum optimization and the non-dominated quantum iterative optimization. However, their complexity is proportional to √N, where N corresponds to the total number of legitimate routes, thus still failing to find the solutions in “polynomial time.” As a remedy, we devise a dynamic programming framework and propose the so-called evolutionary quantum pareto optimization (EQPO) algorithm. We analytically characterize the complexity imposed by the EQPO algorithm and demonstrate that it succeeds in solving the Pareto-optimal routing problem in polynomial time. Finally, we demonstrate by simulations that the EQPO algorithm achieves a complexity reduction, which is at least an order of magnitude when compared to its predecessors, albeit at the cost of a modest heuristic accuracy reduction.

An ideal visible light communication (VLC) system should facilitate reliable data transmission at high throughputs, while also providing flicker-free illumination at the user-defined dimming level. In this spirit, we conceive a unary code aided dimming scheme for ON-OFF keying (OOK) modulated VLC systems. The proposed unary-coded scheme facilitates joint dimming and throughput control, while relying on iterative decoding. It is demonstrated that the proposed unary-coded dimming scheme provides attractive throughput gains over its contemporaries and it is also capable of approaching the theoretical throughput limit. Furthermore, we design novel joint dimming-forward error correction coding schemes, which significantly outperform their compensation time dimming-based counterparts in terms of the attainable bit error rate performance as well as the throughput. Finally, in the quest for approaching the capacity, we also optimize our system using EXTRINSIC information transfer charts and demonstrate an SNR-gain of upto 6 dB over the compensation time dimming-based classic benchmarker.

In this paper, we propose a simple yet powerful mapping scheme that converts any conventional square-matrix-based differential space-time coding (DSTC) into a nonsquare-matrix-based DSTC. This allows DSTC schemes to be used practically in open-loop large-scale multiple-input multiple-output scenarios. Our proposed scheme may be viewed as the differential counterpart of coherent spatial modulation (SM), of the generalized SM, of Bell Laboratories layered space-time architecture, and of subcarrier-index modulation. The fundamental impediment of the existing DSTC schemes is the excessive complexity imposed by the unitary constraint. Specifically, the transmission rate of conventional DSTC schemes decays as the number of transmit antennas increases. Our proposed scheme eliminates this impediment and thus achieves a significantly higher transmission rate. We introduce four novel construction methods for the nonsquare codewords, some of which include an arbitrary number of nonzero elements in each codeword column. Our analysis shows that the proposed encoding technique reduces the complexity of both the inverse Fourier transform and the detection processes. Our proposed scheme is shown to approach the performance of its coherent counterpart for low-mobility scenarios, where the number of transmit antennas is increased up to 256.

In this paper, we propose a combined full duplex (FD)- and half duplex (HD)-based transmission and channel acquisition model for an open-loop multiuser multiple-input multiple-output (MIMO) systems. Assuming residual self-interference at the base station (BS), the idea is to utilize the FD mode during the uplink (UL) training phase in order to achieve simultaneous downlink (DL) data transmission and UL CSI acquisition. More specifically, the BS begins serving a user when its CSI becomes available, while at the same time, it also receives UL pilots from the next scheduled user. We investigate both zero-forcing (ZF) and maximum ratio transmission MIMO beamforming techniques for the DL data transmission in the FD mode. The BS switches to the HD mode once it receives the CSI of all users and it employs ZF beamforming for the DL data transmission until the end of the transmission frame. Furthermore, we derive closed-form approximations for the lower bounded ergodic achievable rate relying on the proposed model. Our numerical results show that the proposed FD-HD transmission and channel acquisition approach outperforms its conventional HD counterpart and achieves higher data rates.

Unmanned aerial vehicles (UAVs) have gained a significant popularity in the recent past owing to their easy deployability and wide range of applications. In most of the short- and medium-range applications, Wi-Fi is used as the access technology for establishing communication between the ground stations and the UAVs. Although Wi-Fi is known to perform well in most of the scenarios, it is important to note that Wi-Fi has been mainly designed for indoor communication in rich scattering environments, whereas the air-to-ground (A2G) channel is characterized by sparse scattering. Considering this important difference in the channel characteristics, we revisit some of the Wi-Fi features and propose efficient design alternatives. First, we provide a statistical model for the sparse A2G channel and design an optimal time-domain quantizer (TDQ) for its feedback. In contrast to the frequency-domain quantizer (FDQ) of the IEEE 802.11n/ac Standard, the proposed TDQ exploits the time-domain sparsity in the channel and requires about 15 times lesser quantization bits than FDQ. Second, we propose a beamforming (BF) scheme with the aid of full-diversity rotation (FDR) matrices and analytically evaluate its symbol error probability in order to quantify the attainable diversity order. Our numerical simulations demonstrate that the proposed FDR-BF scheme outperforms the relevant benchmark schemes in both coded as well as uncoded scenarios. Specifically, the proposed FDR-BF scheme was observed to attain a signal-to-noise ratio gain as high as 6dB compared with the popular geometric mean decomposition-based BF scheme, when operating at an elevation angle of 7.5°.

We consider two-way amplify-and-forward relaying, where multiple full-duplex user pairs exchange information via a shared full-duplex massive multiple-input multiple-output (MIMO) relay. Most of the previous massive MIMO relaying works maximize the spectral efficiency (SE). By contrast, we maximize the non-convex energy efficiency (EE) metric by approximating it as a pseudo-concave problem, which is then solved using the classic Dinkelbach approach. We also maximize EE of the least energy-efficient user relying on the max-min approach. We also compare SE and EE of the proposed design with existing full-duplex systems and quantify the significant improvement achieved by the proposed algorithm. We also compare EE of the proposed full-duplex system to that of its half-duplex counterparts, and characterize the self-loop and inter-user interference regimes, for which the proposed full-duplex system outperforms the half-duplex ones.

In order to enhance the secrecy performance of the generalized space shift keying (GSSK) visible light communication (VLC) system, in this paper, an optical jamming-aided secrecy enhancement scheme is proposed, in which the source transmitter (S) simultaneously sends both the confidential desired signal and optical jamming signals under the amplitude and power constraints. The optical jamming signals obey the truncated Gaussian distribution for satisfying the constraints. Given the discrete set of channel inputs, the optical jamming-aided GSSK-VLC system's secrecy performance is analyzed. Explicitly, the average mutual information (AMI), the lower bound of AMI and its closed-form approximation as well as the achievable secrecy rate are formulated analytically. Furthermore, the optimal power sharing strategy of the proposed GSSK-VLC systems relying on optical jamming is derived. Closed-form expressions are provided for the optimal power sharing in both the low- and high-SNR regions. Finally, the extensive simulation results are presented to validate our analytical results.

Scalable video streaming over femtocell networks relying on two-tier spectrum-sharing is designed for coping with time-varying channel conditions, stringent video QoS requirements as well as with strong cross-tier interference between the over-sailing macro- and the femtocells. Dynamic video layer selection and resource allocation are invoked to enable the adaptation of the scalable video streaming service to the dynamics of both channel quality and interference price fluctuations. We formulate the design as a constrained stochastic optimization problem, which strikes a compelling compromise between the perceivable quality of experience and the monetary implications of the interference. Since the time scale of resource allocation is more short term than that of the video layer selection, we decompose the original long-term utility optimization problem into a pair of readily tractable subproblems with the aid of two different time-scales by invoking the powerful technique of Lyapunov drift and optimization. By exploiting the specific structure of these subproblems, low-complexity algorithms are derived for dynamic video layer selection and resource allocation, which rely on the near-instantaneously available information rather than on any prior statistical knowledge. Finally, we derive the analytical bounds of the theoretically achievable performance. Experimental results are presented for characterizing the performance attained.

The concept of amorphous-boundary-based user-centric cells invoked for visible light communication (VLC) was shown to offer extra throughput benefits over the conventional network-centric VLC. However, this improvement was quantified based on a number of idealized simplifying assumptions, such as operating exactly at the Shannon capacity. Also, the light emitting diode in VLC was assumed to have no non-linear distortion and no clipping distortion. Furthermore, greedily supporting all the user equipments in the system may in fact reduce the achievable throughput, when the transmit power is restricted. To provide more practical performance estimate, in this paper, the dynamic throughput maximization of user-centric VLC (UC-VLC) systems is investigated under a range of practical considerations, where the number of served UEs, the modulation-mode assignment and the power allocation strategy are all dynamically decided by our proposed heuristic dynamic-programming-based algorithm. Our simulations indicate that both the achievable throughput and the outage probability of the proposed UC-VLC system is better than that of the conventional NC-VLC system, under a range of practical constraints.

In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth.

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts as radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

In order to support high-user-load multiple-access (MA), we propose a non-orthogonal MA scheme based on a beneficial amalgam of spatial modulation (SM) and sparse code-division multiple-access (SCDMA), which is termed the SM-SCDMA. Hence, SM-SCDMA inherits both the merits of SM with single radio-frequency MIMO transceiver implementation and the advantages of SCDMA relying on low-complexity signal detection. In this paper, we evaluate the potential of SM-SCDMA as well as its low-complexity near-optimum signal detection. Given these objectives, we consider both the maximum likelihood detection and the message passing algorithm aided detection (MPAD) that is derived based on the maximum a posteriori principles. In order to evaluate the performance of large SM-SCDMA without relying on time-consuming simulations, we propose new approaches for analyzing the performance of SM-SCDMA systems. A range of formulas that are valid in the signal-to-noise ratio region of practical interest are derived. Finally, the performance of SM-SCDMA systems is investigated by addressing diverse design concerns. Our studies and performance results show that SM-SCDMA constitutes a promising MA scheme for the future ultra dense systems. Assisted by the MPAD, it is capable of supporting high-user-load MA transmission associated with a normalized user-load factor of two.

Spatial-multiplexing aided spatial modulation (SMx-SM) is proposed, which intrinsically amalgamates the concept of vertical bell labs space-time (V-BLAST) and SM to attain a high transmission rate, despite its low number of radio frequency (RF) chains at the transmitter. Specifically, in the SMx-SM scheme, the transmit antennas are partitioned into groups and the SM technique is applied individually to each group. Furthermore, low-complexity threshold-aided compressive sensing-based and message passing-based detectors are derived for our SMx-SM system. Our simulation results show that the proposed SMx-SM system exhibits a better performance despite its lower complexity than the conventional generalized spatial modulation system. More importantly, the proposed SMx-SM system is capable of providing considerable performance gains over the V-BLAST system at the same number of RF chains and throughput. Finally, an upper bound is derived for the average bit error probability, which is confirmed by our simulation results.

In this paper, a radically new anticipatory perspective is taken into account when designing the user-to-access point (AP) associations for indoor visible light communications (VLC) networks, in the presence of users' mobility and wireless-traffic dynamics. In its simplest guise, by considering the users' future locations and their predicted traffic dynamics, the novel anticipatory association prepares the APs for users in advance, resulting in an enhanced locationand delayawareness. This is technically realized by our contrived design of an efficient approximate dynamic programming algorithm. More importantly, this paper is in contrast to most of the current research in the area of indoor VLC networks, where a static network environment was mainly considered. Hence, this paper is able to draw insights on the performance tradeoff between delay and throughput in dynamic indoor VLC networks. It is shown that the novel anticipatory design is capable of significantly outperforming the conventional benchmarking designs, striking an attractive performance trade-off between delay and throughput. Quantitatively, the average system queue backlog is reduced from 15 to 8 [ms], when comparing the design advocated to the conventional benchmark at the peruser throughput of 100 [Mbps], in a 15 × 15 × 5 [m3] indoor environment associated with 8 × 8 APs and 20 users walking at 1 [m/s].

In order to meet the demands of “Internet above the clouds,” we propose a multiple-antenna aided adaptive coding and modulation (ACM) for aeronautical communications. The proposed ACM scheme switches its coding and modulation mode according to the distance between the communicating aircraft, which is readily available with the aid of the airborne radar or the global positioning system. We derive an asymptotic closed-form expression of the signal-to-interference-plus-noise ratio (SINR) as the number of transmitting antennas tends to infinity, in the presence of realistic co-channel interference and channel estimation errors. The achievable transmission rates and the corresponding mode-switching distance-thresholds are readily obtained based on this closed-form SINR formula. Monte-Carlo simulation results are used to validate our theoretical analysis. For the specific example of 32 transmit antennas and four receive antennas communicating at a 5-GHz carrier frequency and using 6-MHz bandwidth, which are reused by multiple other pairs of communicating aircraft, the proposed distance-based ACM is capable of providing as high as 65.928-Mb/s data rate when the communication distance is less than 25 km.

This paper proposes a novel symbol-to-bit demapping algorithm for Gray-labeled phase shift keying (PSK) constellations. Unlike the Max-Log-MAP demapper, the proposed algorithm does not perform exhaustive search operations, but directly computes the soft information by exploiting the binary search and the symmetry of the Gray-labeled PSK constellations. Hence its complexity is remarkably reduced from the order of O(2m) of the Max-Log-MAP to O(M), where M denotes the number of bits per symbol. A pair of recent methods are used as bench markers in this paper. One of them reduces the complexity without any performance loss, while the other is a recursive method that reduces the complexity by approximating the original bit metric. It is shown that the proposed algorithm achieves a more substantial complexity reduction than the former bench marker. The proposed algorithm achieves a similar complexity reduction to the latter bench marker and yet, it does not suffer from any performance degradation.

In this paper, we analyze the effect of time-varying channels on the performance of a non-coherent massive single-input multiple-output (SIMO) uplink system based on M-ary Differential Phase Shift Keying (M-DPSK), when amalgamated with bit-interleaved coded modulation relying on iterative decoding (BICM-ID). Additionally, we study the number of receive antennas (R) required in hostile time-varying channels for attaining a similar performance to that achieved for stationary channels. Furthermore, we analyze the maximum achievable rate (MAR) in conjunction with the specific modulation and coding schemes considered. The analysis is based on extrinsic information transfer (EXIT) charts parametrized by R, which varies with the signal to interference-plus-noise ratio (SINR). Our numerical results show that the system is robust to the effects of the channel's temporal correlation and that the simulation-based MAR closely matches the semi-analytical achievable rate obtained using EXIT charts. Hence,our system can be applied to scenarios associated with the short coherence time of high-speed railway systems, for example.

The millimeter wave (mmWave) frequency band offers substantial hitherto unused spectral resources for future wireless communication systems in order to meet the increasing capacity demand. However, mmWave frequencies suffer from high propagation losses, which may be mitigated by directional beamforming in addition to baseband precoding. This is usually referred to as hybrid beamforming. In this paper, we investigate the so-called discrete Fourier transform- mutually unbiased bases (DFT-MUB) aided codebook design conceived for limited-feedback mmWave systems, where the MUB aided codebook is applied in the baseband, while the DFT codebook is invoked for RF analog phase shifters. We demonstrate that our DFT- MUB codebook design performs similarly to the optimal digital precoding matrix, where the precoder is selected as the right singular matrix of the channel. However, our solution significantly reduces the search complexity in the baseband, while performing within 2.5 dB from the optimal digital precoder.

The recent concept of beamspace multiple-input multiple-output (MIMO) is capable of significantly reducing the number of radio-frequency (RF) chains required by millimeter-wave (mmWave) massive MIMO systems. However, the fundamental limit of the existing beamspace MIMO is that, the number of supported users cannot be higher than the number of RF chains using the same time-frequency resources. To break this limit, beamspace MIMO is integrated with non-orthogonal multiple access (NOMA) in the proposed MIMO-NOMA system in this paper, where the number of supported users can be higher than the number of RF chains. To reduce the inter-beam interference, a transmit precoding (TPC) scheme based on the principle of zero-forcing (ZF) is designed. Furthermore, a dynamic power allocation scheme is proposed for maximizing the achievable sum rate. Moreover, a low-complexity iterative optimization algorithm is conceived for dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA achieves a higher spectrum and energy efficiency than the existing beamspace MIMO for mmWave communications.

At the time of writing, vehicle-to-vehicle (V2V) communication is enjoying substantial research attention as a benefit of its compelling applications. However, the ever-increasing tele-traffic is expected to result in overcrowding of the available band. As a first resort, multiple input multiple output (MIMO) can be utilized to enhance the attainable bandwidth efficiency or link reliability. However, in hostile V2V wireless propagation environments, the achievable multiple-antenna gain is eroded by the channel correlation. As a promising MIMO technique, spatial modulation (SM) only activates a single transmit antenna (TA) in any symbol interval and, hence, completely avoids the inter-antenna interference, hence showing robustness against channel correlation. As a further powerful solution, non-orthogonal multiple access (NOMA) has been proposed for improving the bandwidth efficiency. Inspired by the robustness of SM against channel correlation and the benefits of NOMA, we intrinsically amalgamate them into NOMA-SM in order to deal with the deleterious effects of wireless V2V environments as well as to support improved bandwidth efficiency. Moreover, the bandwidth efficiency of NOMA-SM is further boosted with the aid of a massive TA configuration. Specifically, a spatio-temporally correlated Rician channel is considered for a V2V scenario. We investigate the bit error ratio performance of NOMA-SM via Monte Carlo simulations, where the impact of the Rician K-factor, spatial correlation of the antenna array, time-varying effect of the V2V channel, and the power allocation factor is discussed. Furthermore, we also analyze the capacity of NOMA-SM. By analyzing the capacity and deriving closed-form upper bounds on the capacity, a pair of power allocation optimization schemes are formulated. The optimal solutions are demonstrated to be achievable with the aid of our proposed algorithm. Again, instead of simply invoking a pair of popular techniques, we intrinsically amalgamate SM and NOMA to conceive a new system component exhibiting distinct benefits in the V2V scenarios considered.

In a cooperative network, the user equipment (UE) shares information for cooperatively achieving a common goal. However, owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on modeling the UEs' decision making behavior, namely as to whether they are willing/able to share genuine information, even though this directly affects the information credibility across the network. Hence, we propose a game theoretic framework for the associated information credibility modeling by taking into account the users' information sharing strategies and utilities. This framework is investigated under both a homogeneous model and a heterogeneous model. The spontaneous information credibility equilibria of both models are derived and analyzed, including the closed-form analysis of the homogeneous model based on a sophisticated evolutionary game model and on the reinforcement learning-based analysis of the heterogeneous model. Moreover, a credit mechanism is designed for encouraging the UEs to share genuine information. Experimental results relying on real-world data traces support our utility function formulation, while our simulation results verify the theoretical analysis and show that all the UEs are encouraged by the proposed algorithm to share genuine information with a probability of one, when a credit mechanism is invoked. The proposed modeling techniques may be applied in diverse cooperative networks, including classic wireless networks, vehicular networks, as well as social networks.

Welcome to the third one in the sequel of three IEEE JSAC special issues on Spectrum Sharing and Aggregation for Future Wireless Networks. In recognition of the fact that a substantial number of submissions have been received in response to the call for papers, the decision has been made to publish three issues on the cutting-edge advances in spectrum sharing and aggregation. The first two issues were published in October 2016 with 20 papers and November 2016 with 19 papers, respectively. This is the third issue with 17 papers, covering a feast of hot research topics as follows.

Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.

For the sake of supporting massive connectivity in the future 5G networks, non-orthogonal multiple access (NOMA) techniques are advocated. As a promising NOMA technique, in recent years sparse code multiple access (SCMA) has attracted substantial attention. However, there is a paucity of studies on the theoretical analysis of its error-freely achievable data rate, especially, in the downlink context. Hence, we derive the cutoff rate of SCMA in downlink broadcast channels, which indicates the lower-bound of a system's error-freely achievable rate. However, we will demonstrate that when considering the conventional categorization of pairwise error events, the accuracy of the cutoff rate rapidly degrades in the low-SNR region owing to the fact that multi-user SCMA systems typically encounter an extremely large constellation size. Alternatively, by invoking Bergmans' concept from 1973 in the categorization of pairwise error events, we obtain a more accurate cutoff rate both in the low- SNR regions and the high-SNR regions. Moreover, we provide insights into the cutoff rate derivation process, which reveals some general guidelines for designing a beneficial codebook, capable of improving SCMA with respect to its original low-density signature-based counterpart.

We show that certain signal constellations invoked for classic differential encoding result in a phenomenon we term as the unbounded differential constellation size (UDCS). Various existing differential transmission schemes that suffer from this issue are identified. Then, we propose an enhanced algebraic field extension-based differential spatial modulation (AFE-DSM) scheme and its enhanced counterpart that strikes a diversityrate tradeoff (AFE-DSM-DR), both of which overcome the UDCS issue without compromising its full transmit diversity advantage. Furthermore, the proposed schemes are extended to incorporate amplitude and phase shift keying (APSK) in order to exploit all the available degrees of freedom. Additionally, we propose a pair of detection schemes specially designed for APSK-aided differential transmission schemes. Explicitly, we conceive the buffered minimum mean squared error (B-MMSE) detector and buffered maximum likelihood (B-ML) detector, which exploit the knowledge of previously detected symbols in order to further improve the detection performance. Our simulation results have shown that the proposed detectors are capable of bridging the performance gap between the conventional differential detector (CDD) and the coherent detector that has full channel state information. Specifically, when employing the proposed APSKaided AFE-DSM scheme operating at a rate of 2 b per channel use, the B-MMSE and B-ML detectors are observed to give about 3and 3.5-dB signal-to-noise ratio gain with respect to their CDD counterpart at a bit error ratio of 10-5.

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency (EE) of a multiuser multiple-input multiple-output-aided distributed antenna system. The fronthaul link's power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints, some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the EE optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Second, a triple-layer iterative algorithm is proposed for solving the approximated EE optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened EE optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms are capable of achieving an order of magnitude higher EE than its conventional counterpart operating without considering link selection.

Massive spatial modulation aided multiple-input multiple-output (SM-MIMO) systems have recently been proposed as a novel combination of SM and of conventional massive MIMO, where the base station (BS) is equipped with a large number of antennas and simultaneously serves multiple user equipment (TIE) that employ SM for their uplink transmission. Since the massive SM-MIMO concept combines the benefits of both the SM and massive MIMO techniques, it has recently attracted substantial research interest. In this paper, we study the achievable uplink spectral efficiency (SE) of a multi-cell massive SM-MIMO system, and derive closed-form expressions to asymptotically lower-bound the SE yielded by two linear BS combining schemes, including maximum ratio combining and zero forcing combining, when a sufficiently large number of BS antennas are equipped. The derivation takes into account the impact of transmitter spatial correlations, imperfect channel estimations, user-specific power controls, and different pilot reuse factors. The proposed asymptotic bounds are shown to be tight, even when the scale of BS antennas is limited. The new SE results facilitate a system-level investigation of the optimal number of uplink transmit antennas (TAs) N with respect to SE maximization. Explicitly, we provide theoretical insights on the SE of massive SM-MIMO systems. Furthermore, we demonstrate that massive SM-MIMO systems are capable of outperforming the SE of conventional massive MIMOs relying on single-TA TIEs.

This paper investigates the physical layer security of non-orthogonal multiple access (NOMA) in large-scale networks with invoking stochastic geometry. Both single-antenna and multiple-antenna aided transmission scenarios are considered, where the base station (BS) communicates with randomly distributed NOMA users. In the single-antenna scenario, we adopt a protected zone around the BS to establish an eavesdropper-exclusion area with the aid of careful channel ordering of the NOMA users. In the multiple-antenna scenario, artificial noise is generated at the BS for further improving the security of a beamforming-aided system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both single-antenna and multiple-antenna aided scenarios. For the single-antenna scenario, we perform secrecy diversity order analysis of the selected user pair. The analytical results derived demonstrate that the secrecy diversity order is determined by the specific user having the worse channel condition among the selected user pair. For the multiple-antenna scenario, we derive the asymptotic secrecy outage probability, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided for verifying the analytical results derived and to show that: 1) the security performance of the NOMA networks can be improved by invoking the protected zone and by generating artificial noise at the BS and 2) the asymptotic secrecy outage probability is close to the exact secrecy outage probability.

Full-dimensional multiple-input multiple-output (FD-MIMO) systems, whereby each base station is equipped with a uniformly spaced rectangular antenna array (URA), provides a practical means of realizing massive MIMO systems. However, the spectral efficiency of URA is considerably lower than that of its uniformly spaced linear array counterpart having the same number of antenna elements. In this paper, we first introduce a discrete angular resolution metric for quantifying the low resolution of URA in the antenna-elevation domain. This motivates us to propose a novel antenna device design, referred to as the structured non-uniformly spaced rectangular array (NURA), in which the antenna elements are non-uniformly distributed in the elevation-angle domain. Specifically, we conceive a structured NURA device for which the nonuniform distribution of the elevation-domain antenna elements is controlled by a single parameter. The design of the optimally structured NURA for the given nonlinear antenna-element-positioning function then becomes a single-parameter optimization, namely, that of maximizing the spectral efficiency of the FD-MIMO system, which can be solved efficiently. Our simulation results demonstrate that our structured NURA design significantly outperforms the standard URA in terms of achievable spectral efficiency. Our proposed structured NURA design therefore offers an effective practical framework for enhancing the achievable performance of FD-MIMO systems.

Considering the dearth for spectrum in the congested microwave band, the next generation of cellular communication systems is envisaged to incorporate part of the millimeter wave (mm-wave) band. Hence, recently, there has been a significant interest in beamforming aided mm-wave systems. We consider a downlink multiuser mm-wave system employing a large number of antennas combined with fewer radio frequency chains both at the base station (BS) and at each of the user equipments (UEs). The BS and each of the UE are assumed to have a hybrid beamforming architecture, where a set of analog phase shifters is followed by digital precoding/combining blocks. In this paper, we propose an iterative matrix decomposition based hybrid beamforming (IMD-HBF) scheme for a single-user scenario, which accurately approximates the unconstrained beamforming solution, we show that the knowledge of the angle of departure (AoD) of the various channel paths is sufficient for the block diagonalization (BD) of the downlink mm-wave channel and hence for achieving interference free channels for each of the UEs, we propose a novel subspace projection based AoD aided BD (SP-AoD-BD) that achieves significantly better performance than the conventional BD, while still only requiring the knowledge of the AoD of various channel paths, and we use IMD-HBF in order to employ SP-AoD-BD in the hybrid beamforming architecture and study its performance with respect to the unconstrained system. We demonstrate using simulation results that the proposed IMD-HBF gives the same spectral efficiency as that of the unconstrained system in the single user scenario. Furthermore, we study the achievable sum rate of the users, when employing SP-AoD-BD with the aid of IMD-HBF and show that the loss in the performance with respect to the unconstrained system as well as the existing schemes is negligible, provided that the number of users is not excessive.

This paper investigates the design of robust nonlinear transceivers conceived for multiple-input multiple-output full-duplex wireless-powered relay networks in the face of realistic imperfect channel state information (CSI). A novel self-energy recycling aided relaying protocol is employed, whereby the relay node benefits from energy harvesting (EH) gleaned from the self-interfering link in addition to its primary energy. The proposed nonlinear transceiver relies on a Tomlinson-Harashima (TH) precoder along with an amplify-and-forward (AF) relaying matrix and a linear receiver, where the TH precoder is composed of a feedback matrix and a source precoding matrix. Two different criteria are considered for the robust design of the nonlinear transceiver in the presence of channel estimation errors modeled by the Gaussian distribution. The first one aims to minimize the mean-squared-error (MSE) at the destination subject to a transmit power constraint at the source and an EH constraint at the relay. The resultant optimization problem is converted to four subproblems and solved via an alternating optimization (AO) algorithm that iteratively updates the transceiver coefficients by sequentially addressing each subproblem, while keeping the other matrix variables fixed. The second design criterion aims to minimize the transmit power at the source under both MSE and EH constraints. Similarly, an AO-based iterative algorithm is proposed for solving this problem. Our simulation results show that the robust design advocated is capable of alleviating the effects of CSI errors, hence improving the robustness of the system over that of the corresponding linear designs.

In this paper, the energy-spectral efficiency (ESE) benefiting from the joint optimization of coordinated multi-point (CoMP) transmission and base station (BS) deployment is evaluated in the context of dense large-scale cellular network. We first derive a closed-form network ESE expression for a large-scale CoMP-enhanced network, which allows us to quantify the influence of key network parameters on the achievable network ESE, including the BS density and the cooperation activation probability, characterized by a CoMP activation factor as well as the users' behaviors, such as their geographical mobile-traffic intensity and average user rate. With the aid of this tractable ESE expression and for a given BS density, we next formulate a cellular-scenario-aware CoMP activation optimization problem while considering the users' outage probability as constraints to maximize the network's ESE. We then jointly optimize the CoMP activation factor and the BS density to maximize the network ESE, again under the constraint of the users' outage probability. Our simulation results confirm the accuracy of our analysis and verify the impact of several key parameters on the network ESE. Finally, the ESE improvement of our proposed strategies is evaluated under diverse scenarios, which provides valuable insight into the joint CoMP and BS deployment optimization in dense large-scale cellular networks.

Visible light communication (VLC) may be expected to become a new member in the small-cell family of the next-generation heterogeneous network landscape for complementing the overloaded radio frequency band. In order to further improve the performance of our user-centric (UC) cluster formation design proposed in our previous work, the quality-of-service (QOS) metric in terms of statical delay requirements is considered, when constructing the UC clusters. Inspired by the concept of effective capacity (EC), we formulate the proportional-fairness-based utility function of each optical link, while satisfying the statistical delay requirement. Upon maximizing the sum utility of the system, the joint cluster formation and user association problem is solved by our proposed greedy algorithm, which is capable of providing an average user EC of about 90% of the optimal value found via exhaustive search. Furthermore, the performance of the UC cluster formation is superior than the traditional cellular designs in all scenarios considered.

A novel transmission protocol is conceived for a multi-user, multi-relay, multiple-input-multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input-single-output (MISO) channels, which are spatially multiplexed for transmission. For the sake of improving the attainable capacity, these MISO channels are grouped using a pair of novel grouping algorithms, which are then evaluated in terms of their performance versus complexity trade-off1.

Space shift keying (SSK) transmission is a low-complexity complement to spatial modulation (SM) that solely relies on a spatial-constellation diagram for conveying information. The achievable performance of SSK is determined by the channel conditions, which in turn define the minimum Euclidean distance (MED) of the symbols in the received SSK constellation. In this contribution we concentrate on improving the power efficiency of SSK transmission via symbol pre-scaling. Specifically, we pose a pair of related optimization problems for a) enhancing the MED at reception while satisfying a given power constraint at the transmitter, and b) reducing the transmission power required for achieving a given MED. The resultant optimization problems are NP-hard, hence they are subsequently reformulated and solved via semidefinite programming. The results presented demonstrate that the proposed pre-scaling strategies are capable of enhancing the attainable performance of conventional SSK, while simultaneously extending its applicability and reducing the complexity of the existing pre-scaling schemes.

Channel estimation is conceived for optical wireless scattering channels associated with Laser Diode transmitters and photon-counting/photomultiplier tube receivers. The proposed channel estimation approach consists of two stages, namely the estimation of the channel tap second-order moments followed by the estimation of the channel taps based on the estimate of second-order moments. In the first stage, we provide the general framework of the moment estimation complemented by the conception of an estimation approach based on a sparse pilot structure, as well as by the analysis of the estimation error. In the second stage, we conceive the channel tap estimation based on the eigenvalue decomposition of the matrix of estimated second-order moments, and analyze the associated performance. It is shown that as the length of the pilot sequence approaches infinity, the probability of having an estimation distortion above a threshold can be arbitrarily small. Simulation results show that a sparse pilot sequence can lead to a smaller estimation error than its counterpart using random 0-1 bits.

The last decade has witnessed a booming era of wireless vehicular networks, supporting diverse road traffic services and applications. Information dissemination/sharing among vehicles is the fundamental goal of vehicular networks. Although diverse information dissemination/sharing mechanisms have been proposed in the existing literature, the physical layer outage performance of information sharing has not been analyzed. Against this background, in this paper, we study the outage probability of road traffic information sharing in underlay cognitive vehicular networks under both a general scenario and a specific highway scenario. The general scenario relies on the Nakagami-m channel, while the highway scenario is its special case associated with the Rayleigh fading channel. Moreover, we also invoke a real-world dataset containing the locations of Beijing taxis to conduct simulations, the results of which verify the accuracy of our theoretical analysis.

We explore a bandwidth efficient transmission scheme that amalgamates multiple-input-multiple-output spatial multiplexing (SMX) with receive antenna based spatial modulation (RSM). The RSM here is applied to the combined spatial and power-level domain, not by activating and de-activating the receive antennas, but rather by choosing between two power levels {P1,P2} for the received symbols in these antennas, such that all receive antennas are active and SMX can still be accommodated. This allows for the coexistence of RSM with SMX and the results show an increased bandwidth efficiency for the proposed scheme compared to both SMX and RSM. We further carry out a mathematical analysis to optimize the ratio between P1and P2for attaining the minimum error rates. Our analytical and simulation results demonstrate significant bandwidth efficiency gains for the proposed scheme compared to conventional SMX and RSM.

In this paper, we propose a cognitive Go-Back-N Hybrid Automatic Repeat reQuest (CGBN-HARQ) scheme for a cognitive radio (CR) system to opportunistically transmit data over a primary radio (PR) channel. We model the activity of PR users (PRUs) occupying the PR channel as a Markov chain with two states: `ON' and `OFF'. In order to use the PR channel, the CR system first senses the availability/unavailability of the PR channel. Once it finds that the PR channel is free, the CR system transmits data packets over the PR channel's spectrum, whilst relying on the principles of GBN-HARQ. In this paper, we investigate both the throughput and delay of CGBN-HARQ, with a special emphasis on the impact of various system parameters involved in the scenarios of both perfect and imperfect spectrum sensing. Our studies demonstrate that the activity of PRUs, the transmission reliability of the CR system as well as the number of packets transmitted per time-slot may have a substantial impact on both the throughput and the delay of the CR system.

As a promising technique, cooperative relaying has attracted more and more attention from academia and industry recently. In this paper, we investigate the scheme of Decode-and-Forward Two-way Relaying (DF-TWR) relying on a cross-layer design, which combines adaptive Network-coded Modulation (NCM) at the physical layer and truncated Automatic Repeat reQuest (ARQ) at the data link layer. The relay node utilizes Network-Coded Quadrature amplitude modulation (NC-QAM) where NCM imposes only a modest signal-to-noise ratio (SNR) degradation on the single-link QAM performance. Additionally, we derive the achievable spectral efficiency in closed-form for transmission over Rayleigh fading channels. It is shown that this combination of adaptive NC-QAM and truncated ARQ substantially improves the system's throughput compared to the schemes operating without ARQ.

The recently developed orthogonal frequency-division multiplexing relying on index modulation (OFDM-IM) is adopted to the in-vehicle power line communications (PLCs) in order to combat the deleterious effects of frequency-selective fading and impulsive noise, whilst improving the energy efficiency of data communications. Furthermore, the low density parity check (LDPC) coding is invoked to further enhance the reliability of in-vehicle PLCs, which is of particular importance in light-weight airborne vehicles. For aiding LDPC decoding, a reduced complexity soft-decision detection scheme is proposed. The performance of the LDPC-coded OFDM-IM system is studied by simulation, when assuming communications over in-vehicle PLC channels. Our studies show that LDPC-coded OFDM-IM is capable of combating frequency-selective fading, mitigating impulsive noise, as well as striking a compelling trade-off between the spectral efficiency and energy efficiency for in-vehicle PLCs.

In a cooperative network the user equipment (UE) share information with each other for cooperatively achieving a common goal. While owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Hence diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on the UEs' information sharing inclination, even though this directly affects the information credibility across the network. In this paper, we propose a general framework for the information credibility modelling of cooperative networks by taking into account the users' information sharing inclinations. Specifically, the utility functions of sharing both genuine and false information are defined. Based on this utility formulation and its closed-form analysis, the spontaneous information credibility equilibrium is derived. Our simulation results verify the accuracy of our theoretical analysis.

Hierarchical modulation (HM) is a layered modulation scheme, which is widely employed by the telecommunication industry. The higher flexibility and lower complexity of the HM scheme has its dramatic benefits for wireless communications, hence the achievable performance of cooperation‐aided coded HM has drawn substantial research interests. In this study, a triple‐layer HM‐aided four‐node cooperative communication system is proposed, and its discrete‐input continuous‐output memoryless channel capacity is derived, which is used for finding the optimal position of the relay nodes as well as to design appropriate HM constellations. The authors’ simulation results show that if a rate‐1/2 ‘perfect’ channel code is assumed, the four‐node network becomes capable of conveying a coded HM‐64QAM signal in three time slots at an average signal‐to‐noise ratio of −0.71 dB.

Optical single sideband (OSSB) signals are conventionally generated using a dual‐drive Mach–Zehnder modulator (MZM) or by using a single‐drive MZM in conjunction with a fibre Bragg grating filter. In this study, the authors propose and mathematically characterise a novel radio over fibre downlink in which an OSSB signal is generated using a single‐drive, rather than dual‐drive, MZM without any additional filter. Additionally, their proposed scheme can achieve optical upconversion of the electronic signal. Furthermore, in addition to cost advantages, the proposed scheme's bit error ratio (BER) performance is better than the classical OSSB signal generation scheme.

In this study, the authors consider a two‐hop network, where multiple source nodes (SNs) transmit to a destination node (DN) with the aid of a relay node (RN). The RN is equipped with a buffer, which is capable of storing multiple frames received from the SNs. During each time slot, the proposed protocol activates either the SN–RN hop or the RN–DN hop, depending on the channel quality of each hop and the buffer state at the RN. To optimise the hop activation for the network, they design a hop quality metric and propose a multi‐user buffer‐aided‐relaying uplink (MU‐BR‐UL) protocol, with the aid of the minimum signal‐to‐noise power ratio approximation. The benefits of the proposed protocol are analysed in terms of the end‐to‐end (e2e) outage probability and the e2e transmission delay. Then, the optimal power allocation is proposed for minimising the e2e outage probability under the total power constraint. The results indicate that the outage performance is significantly improved when the proposed power allocation is utilised in the MU‐BR‐UL protocol.

Hidden semi‐Markov modelling of the asynchronous impulsive noise (IN) encountered in indoor broadband power line communications (PLCs) is investigated by considering the statistical distributions of both the inter‐arrival time and the duration of asynchronous IN components. Then, the bit error ratio (BER) of orthogonal frequency division multiplexing systems using Q ‐ary quadrature amplitude modulation is analysed with the aid of the proposed noise model, when communicating over dispersive indoor power line channels inflicting asynchronous IN in addition to the background noise. The authors’ simulation results confirm the accuracy of the analysis and quantify the impact of various factors on the achievable BER performance. The grave impact of asynchronous IN on indoor broadband PLCs suggests that efficient techniques have to be designed for mitigating its effects.

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

Welcome to the IEEE JSAC special issue on Spectrum Sharing and Aggregation for Future Wireless Networks. The conception of this special issue is motivated by the following observations: the ever-increasing penetration of both the mobile Internet and of the Internet-of-things is gradually clogging up the most valuable spectral bands available in the sub-2 GHz frequency range for future wireless networks. Hence there is an urgent need for improved spectrum exploitation to satisfy this demand. It is expected that the wireless tele-traffic will continue to grow quite dramatically in the ensuing years, hence further widening the spectrum-supply versus demand gap. In order to mitigate this gap, spectrum sharing and aggregation have been well recognized as promising approaches, which led to rapid advances by harnessing a large cross-section of the research community. Nonetheless, there are numerous unsolved technical challenges. This special issue aims for reporting on some of these cutting-edge advances in spectrum sharing and aggregation, whilst opening new avenues of research in this area.

The papers in this special issue represent the second one in the sequel of three special issues on spectrum sharing and aggregation for future wirelessn networks.

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users' decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms.

In this paper, we design an energy efficient indoor visible light communications (VLC) system from a radically new perspective based on an amorphous user-to-network association structure. Explicitly, this intriguing problem is approached from three inter-linked perspectives, considering the cell formation, link-level transmission and system-level optimisation, critically appraising the related optical constraints. To elaborate, apart from proposing hitherto unexplored amorphous cells (A-Cells), we employ a powerful amalgam of asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) and transmitter pre-coding aided multi-input single-output (MISO) transmission. As far as the overall system-level optimisation is concerned, we propose a low-complexity solution dispensing with the classic Dinkelbach's algorithmic structure. Our numerical study compares a range of different cell formation strategies and investigates diverse design aspects of the proposed A-Cells. Specifically, our results show that the A-Cells proposed are capable of achieving a much higher energy efficiency per user compared to that of the conventional cell formation for a range of practical field of views (FoVs) angles.

The family of conventional half-duplex (HD) wireless systems relied on transmitting and receiving in different time slots or frequency subbands. Hence, the wireless research community aspires to conceive full-duplex (FD) operation for supporting concurrent transmission and reception in a single time/frequency channel, which would improve the attainable spectral efficiency by a factor of two. The main challenge encountered in implementing an FD wireless device is the large power difference between the self-interference (SI) imposed by the device’s own transmissions and the signal of interest received from a remote source. In this survey, we present a comprehensive list of the potential FD techniques and highlight their pros and cons. We classify the SI cancellation techniques into three categories, namely passive suppression, analog cancellation and digital cancellation, with the advantages and disadvantages of each technique compared. Specifically, we analyze the main impairments (e.g., phase noise, power amplifier nonlinearity, as well as in-phase and quadrature-phase (I/Q) imbalance, etc.) that degrading the SI cancellation. We then discuss the FD-based media access control (MAC)-layer protocol design for the sake of addressing some of the critical issues, such as the problem of hidden terminals, the resultant end-to-end delay and the high packet loss ratio (PLR) due to network congestion. After elaborating on a variety of physical/MAC-layer techniques, we discuss potential solutions conceived for meeting the challenges imposed by the aforementioned techniques. Furthermore, we also discuss a range of critical issues related to the implementation, performance enhancement and optimization of FD systems, including important topics such as hybrid FD/HD scheme, optimal relay selection and optimal power allocation, etc. Finally, a variety of new directions and open problems associated with FD technology are pointed out. Our hope is that this treatise will stimulate future research efforts in the emerging field of FD communications.

Due to the broadcast nature of radio propagation, the wireless air interface is open and accessible to both authorized and illegitimate users. This completely differs from a wired network, where communicating devices are physically connected through cables and a node without direct association is unable to access the network for illicit activities. The open communications environment makes wireless transmissions more vulnerable than wired communications to malicious attacks, including both the passive eavesdropping for data interception and the active jamming for disrupting legitimate transmissions. Therefore, this paper is motivated to examine the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity, and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state of the art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. Several physical-layer security techniques are reviewed and compared, including information-theoretic security, artificial-noise-aided security, security-oriented beamforming, diversity-assisted security, and physical-layer key generation approaches. Since a jammer emitting radio signals can readily interfere with the legitimate wireless users, we also introduce the family of various jamming attacks and their countermeasures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer, and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.

Channel estimation is conceived for optical wireless scattering channels associated with laser diode transmitters and photon-counting/photomultiplier tube receivers. The proposed channel estimation approach consists of two stages, namely, of the estimation of the channel tap second-order moments followed by the estimation of the channel taps based on the estimate of second-order moments. In the first stage, we provide the general framework of the moment estimation complemented by the conception of an estimation approach based on a sparse pilot structure, as well as by the analysis of the estimation error. We also propose a novel sparse pilot design as well as the associated low-complexity channel estimation, and prove the optimality of the proposed channel estimation. In the second stage, we conceive the associated channel tap estimation based on the eigenvalue decomposition of the matrix of estimated second-order moments, and analyze the associated performance. It is shown that as the length of the pilot sequence tends to infinity, the probability of having an estimation distortion above a certain threshold can be reduced arbitrarily small. Simulation results show that the proposed sparse pilot sequence can lead to a smaller estimation error than the pilot design using random 0-1 bits.

Device-to-device (D2D) communication underlying cellular networks, allows direct transmission between two devices in each other's proximity that reuse the cellular resource blocks in an effort to increase the network capacity and spectrum efficiency. However, this imposes severe interference that degrades the system's performance. This problem may be circumvented by incorporating fractional frequency reuse (FFR) or soft frequency reuse (SFR) in OFDMA cellular networks. By carefully considering the downlink resource reuse of the D2D links, we propose beneficial frequency allocation schemes, when the macrocell has employed FFR or SFR as its frequency reuse technique. The performance of these schemes is quantified using both the analytical and simulation results for characterizing both the coverage probability and the capacity of D2D links under the proposed schemes that are benchmarked against the radical unity frequency reuse scheme. The impact of the D2D links on the coverage probability of macrocellular users (CUs) is also quantified, revealing that the CUs performance is only modestly affected under the proposed frequency allocation schemes. Finally, we provide insights concerning the power control design in order to strike a beneficial tradeoff between the energy consumption and the performance of D2D links.

Based on the common interest of mobile users (MUs) in a social group, the dissemination of content across the social group is studied as a powerful supplement to conventional cellular communication with the goal of improving the delay performance of the content dissemination process. The content popularity is modeled by a Zipf distribution to characterize the MUs' different interests in different contents. The factor of altruism (FA) terminology is introduced for quantifying the willingness of content owners to share their content. We model the dissemination process of a specific packet by a pure-birth-based Markov chain and evaluate the statistical properties of both the network's dissemination delay as well as of the individual user-delay. Compared to the conventional base station (BS)-aided multicast, our scheme is capable of reducing the average dissemination delay by about 56.5%. Moreover, in contrast to the BS-aided multicast, increasing the number of MUs in the target social group is capable of reducing the average individual user-delay by 44.1% relying on our scheme. Furthermore, our scheme is more suitable for disseminating a popular piece of content.

Waterfilling problems subjected to peak power constraints are solved, which are known as cave-filling problems (CFP). The proposed algorithm finds both the optimum number of positive powers and the number of resources that are assigned the peak power before finding the specific powers to be assigned. The proposed solution is non-iterative and results in a computational complexity, which is of the order of M, O(M), where M is the total number of resources, which is significantly lower than that of the existing algorithms given by an order of M2, O(M2), under the same memory requirement and sorted parameters. The algorithm is then generalized both to weighted CFP (WCFP) and WCFP requiring the minimum power. These extensions also result in a computational complexity of the order of M, O(M). Finally, simulation results corroborating the analysis are presented.

Recently, there has been significant research effort toward achieving high data rates in the millimeter wave bands by employing large antenna systems. These systems are considered to have only a fraction of the RF chains compared with the total number of antennas and employ analog phase shifters to steer the transmit and receive beams in addition to the conventional beamforming (BF)/combining invoked in the baseband domain. This scheme, which is popularly known as hybrid BF, has been extensively studied in the literature. To the best of our knowledge, all the existing schemes focus on obtaining the BF/combining matrices that maximize the system capacity computed using a Gaussian input alphabet. However, this choice of matrices may be suboptimal for practical systems, since they employ a finite input alphabet, such as quadrature amplitude modulation/phase-shift keying constellations. Hence, in this paper, we consider a hybrid BF/combining system operating with a finite input alphabet and optimize the analog as well as digital BF/combining matrices by maximizing the mutual information (MI). This is achieved by an iterative gradient ascent algorithm that exploits the relationship between the minimum mean-squared error and the MI. Furthermore, an iterative algorithm is proposed for designing a codebook for the analog and digital BF/combining matrices based on a vector quantization approach. Our simulation results demonstrate that the proposed gradient ascent algorithm achieves an ergodic rate improvement of up to 0.4 bits per channel use (bpcu) compared with the Gaussian input scenario. Furthermore, the gain in the ergodic rate achieved by employing the vector quantization-based codebook is about 0.5 bpcu compared with the Gaussian input scenario.

We analytically characterize the data-aided error vector magnitude (EVM) performance of a single-input multiple-output (SIMO) communication system relying on maximal ratio combining (MRC) having either independent or correlated branches that are nonidentically distributed. In particular, exact closed form expressions are derived for the EVM in$\eta\text{-}\mu$fading and$\kappa W\mu$shadowed fading channels and these expressions arevalidatedby simulations. The derived expressions are expressed in terms of Lauricella’s function of the fourth kind$F_D^{(N)}(.)$, which can be easily computed. Furthermore, we have simplified the derived expressions for various special cases such as independent and identically distributed branches, Rayleigh fading, Nakagami-mfading, and$\kappa\text{-}\mu$fading. Additionally, a parametric study of the EVM performance of the wireless system is presented.

The benefits of transmit antenna selection (TAS) invoked for spatial modulation (SM) aided multiple-input multiple-output (MIMO) systems are investigated. Specifically, we commence with a brief review of the existing TAS algorithms and focus on the recently proposed Euclidean distance-based TAS (ED-TAS) schemes due to their high diversity gain. Then, a pair of novel ED-TAS algorithms, termed as the improved QR decomposition (QRD)-based TAS (QRD-TAS) and the error-vector magnitude-based TAS (EVM-TAS) are proposed, which exhibit an attractive system performance at low complexity. Moreover, the proposed ED-TAS algorithms are amalgamated with the low-complexity yet efficient power allocation (PA) technique, termed as TAS-PA, for the sake of further improving the system's performance. Our simulation results show that the proposed TAS-PA algorithms achieve signal-to-noise ratio (SNR) gains of up to 9 dB over the conventional TAS algorithms and up to 6 dB over the TAS-PA algorithm designed for spatial multiplexing systems.

In this paper, we consider the problem of jointly designing transmit precoding (TPC) matrix and receive filter matrix subject to both secrecy and per-transmitter power constraints in the multiple-input multiple-output (MIMO) interference channel, where K legitimate transmitter-receiver pairs communicate in the presence of an external eavesdropper. Explicitly, we jointly design the TPC and receive filter matrices based on the minimum total mean-squared error (MSE) criterion under a given and feasible information-theoretic degrees of freedom. More specifically, we formulate this problem by minimizing the total MSEs of the signals communicated between the legitimate transmitter-receiver pairs, while ensuring that the MSE of the signals decoded by the eavesdropper remains higher than a certain threshold. We demonstrate that the joint design of the TPC and receive filter matrices subject to both secrecy and transmit power constraints can be accomplished by an efficient iterative distributed algorithm. The convergence of the proposed iterative algorithm is characterized as well. Furthermore, the performance of the proposed algorithm, including both its secrecy rate and MSE, is characterized with the aid of numerical results. We demonstrate that the proposed algorithm outperforms the traditional interference alignment algorithm in terms of both the achievable secrecy rate and the MSE. As a benefit, secure communications can be guaranteed by the proposed algorithm for the MIMO interference channel even in the presence of a sophisticated/strong eavesdropper, whose number of antennas is much higher than that of each legitimate transmitter and receiver.

This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-input multiple-output-aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on an optimal pilot design conceived for time-domain channel estimation, which can either completely eliminate PC or significantly reduce it, depending on the channel's coherence time. This is achieved by designing an optimal pilot set allowing us to beneficially group the users in all the cells and to assign a time-shifted pilot transmission to the different groups. Unlike the existing PC elimination schemes, which require an excessively long channel coherence time, our proposed scheme is capable of completely eliminating PC under a much shorter coherence time. Moreover, the existing PC elimination schemes can no longer be used if the channel coherent time is insufficiently large. By contrast, even for extremely short channel coherent time, our scheme can still be implemented to significantly reduce PC. This is particularly beneficial for high velocity scenarios. Our simulation results demonstrate the efficiency of the proposed scheme.

Visible light communication (VLC) combined with advanced illumination may be expected to become an integral part of next-generation heterogeneous networks. In order to mitigate the performance degradation imposed by the intercell-interference (ICI), a user-centric (UC) cluster formation technique employing vectored transmission (VT) is proposed for the VLC down-link system, where multiple users may be simultaneously supported by multiple access points (APs). In contrast to the traditional network-centric (NC) design, the UC-VT cluster formation is dynamically constructed and adjusted, rather than remaining static. Furthermore, we consider the critical issue of multiuser scheduling (MUS) relying on maximizing the “sum utility” of this system, which leads to a joint cluster formation and MUS problem. In order to find a practical solution, the original problem is reformulated as a maximum weighted matching (MWM) problem relying on a user-AP distance-based weight and then a low-complexity greedy algorithm is proposed, which offers a suboptimal yet compelling solution operating close to the optimal value found by the potentially excessive-complexity exhaustive search. Our simulation results demonstrate that the proposed greedy MUS algorithm combined with the UC-VT cluster formation is capable of providing an average user throughput of about 90% of the optimal throughput, which is about three times the throughput provided by the traditional cellular design in some of the scenarios considered.

Turbo detection performed by exchanging extrinsic information between the soft-decision QAM detector and the channel decoder is beneficial for the sake of exploring the bit dependency imposed both by modulation and by channel coding. However, when the soft-decision coherent QAM detectors are provided with imperfect channel estimates in rapidly fading channels, they tend to produce potentially unreliable LLRs that deviate from the true probabilities, which degrades the turbo detection performance. Against this background, in this paper, we propose a range of new soft-decision multiple-symbol differential sphere detection (MSDSD) and decision-feedback differential detection (DFDD) solutions for differential QAM (DQAM), which dispense with channel estimation in the face of rapidly fading channels. Our proposed design aims for solving the two inherent problems in soft-decision DQAM detection design, which have also been the most substantial obstacle in the way of offering a solution for turbo detected MSDSD aided differential MIMO schemes using QAM: 1) how to facilitate the soft-decision detection of the DQAM's amplitudes, which-in contrast to the DPSK phases-do not form a unitary matrix, and 2) how to separate and streamline the DQAM's soft-decision amplitude and phase detectors. Our simulation results demonstrate that our proposed MSDSD aided DQAM solution is capable of substantially outperforming its MSDSD aided DPSK counterpart in coded systems without imposing a higher complexity. Moreover, our proposed DFDD aided DQAM solution is shown to outperform the conventional solutions in literature. Our discussions on the important subject of coherent versus noncoherent schemes suggest that compared to coherent square QAM relying on realistic imperfect channel estimation, MSDSD aided DQAM may be deemed as a better candidate for turbo detection assisted coded systems operating at high Doppler frequencies.

In this paper, we investigate a pair of transmit precoding (TPC) algorithms conceived for spatial modulation (SM) systems communicating over flat-fading multiple-input multiple-output (MIMO) channels. In order to retain all the benefits of conventional SM, we design the TPC matrix to be diagonal and introduce two design criteria for optimizing the elements of the TPC matrix. Specifically, we first investigate a TPC design based on maximizing the minimum Euclidean distance dmin(max-dmin) between the SM signal points at the receiver side. A closed-form solution of the optimal max-dmin-based TPC matrix is derived. Then, another TPC design algorithm is proposed for directly minimizing the bit error ratio (BER) upper bound of SM, which is capable of jointly optimizing the overall Euclidean distance between all received signal points. In the minimum BER (min-BER)-based TPC algorithm, the theoretical gradient of the BER with respect to the diagonal TPC matrix is derived and a simplified iterative conjugate gradient (SCG) algorithm is invoked for TPC optimization. Our simulation results demonstrate that the proposed max-dmin-based TPC algorithm is optimal in terms of the minimum distance. However, increasing dmindoes not achieve a further BER improvement. We also confirm that the min-BER-based TPC outperforms the max-dmin-based TPC schemes in terms of the achievable BER performance.

Developed from the recently emerged generalized pre-coding aided spatial modulation (GPSM) concept, a novel non-linear GPSM scheme based on the powerful vector perturbation philosophy is proposed, where a particular subset of receive antennas is activated and the specific activation pattern itself conveys useful implicit information in addition to the conventional modulated and perturbed symbols. Explicitly, both the infinite and finite alphabet capacities are derived for the proposed non-linear GPSM scheme. The associated complexity, energy efficiency, and error probability are also investigated. Our numerical results show that, as the only known non-linear realization within the spatial modulation family, the proposed scheme constitutes an attractive solution to the flexible design of green transceivers, since it is capable of striking a compelling compromise amongst the key performance indicators of throughput, energy consumption, complexity, and performance. In particular, in the challenging full-rank scenario, conveying information through receive antenna indices exhibits a lower complexity, a higher energy efficiency, and a better error resilience than that of the conventional arrangement.

An uplink transmission for Adaptive Dynamic Network Coding(ADNC) assisted Cooperative Cognitive Radio(CCR) system is proposed for facilitating the recovery of the source information received from the Primary Users (PUs) at the BS. The Cognitive Users (CUs) acting as Relay Nodes invoke the ADNC technique, where the CCR- based control information is exchanged between the CUs and the BS. % The network encoder may be activated in its adaptive mode for the sake of supporting the CUs, depending on the Boolean value of the feedback flags generated by the receiver based on the success/failure of the Adaptive Turbo Trellis Coded Modulation (ATTCM) channel decoder and of the network decoder. As a result, our novel ATTCM-ADNC-CCR system constructed based on a holistic approach is capable of providing an increased throughput, despite reducing the transmission-period of the PU. This reduced transmission-period can also be directly translated into an increased time-duration for the secondary communications of the CUs.

Multiple-input multiple-output (MIMO) amplify-and- forward (AF) relaying is designed for secure communication between a source-destination pair in the presence of multiple eavesdroppers. Assuming statistical knowledge of the eavesdroppers' channel state information (ECSI) errors, we introduce a probabilistically robust design method, which aims to optimize the source transmission power and AF relaying matrix by maximizing the received signal-to- interference-plus-noise ratio (SINR) at the destination, while satisfying a set of intercept probability constraints. The resultant optimization problem becomes nonconvex, and hence we propose a conservative two-step solution, where the source transmission power and the relaying matrix are sequentially optimized. Our simulation results demonstrate the improved secrecy of the proposed relaying design against eavesdropping and its robustness against the channel uncertainties.

A coordinated multi-point visible light communication (CoMP-VLC) downlink is investigated, where the time-frequency (TF) resources are shared by a group of users relying on space division multiple access (SDMA). Linear zero-forcing (ZF) transmit precoding (TPC) is tailored to the CoMP-VLC system in order to eliminate the inter-user interference while accommodating the linear operating region of the light emitting diodes (LEDs). A SDMA user-group sharing the same TF has to host users having sufficiently different user-signatures, i.e. channel gains in a non-dispersive VLC channel. Hence we develop efficient resource allocation (RA) and SDMA user-grouping algorithms in the context of VLC systems that use intensity modulation and direct detection (IM/DD). Finding the optimal SDMA user-group requires an exhaustive search (ES), which has exponentially increasing complexity as the number of users increases. Therefore, a low-complexity user-grouping method is conceived and compared to three existing algorithms in terms of the attainable area spectral efficiency (ASE) and throughput fairness. Our simulation results demonstrate that the proposed algorithm gives a better performance-fairness trade-off than existing benchmarks.

A non-coherent detection assisted Differential Phase Shift Keying aided large-scale MIMO system is designed in a wireless uplink where multiple single-antenna users are transmitting to the base station's receiver equipped with a very large number of receive antennas. We show that the signal to interference plus noise ratio (SINR) scales with the number of receive antennas, which confirms the same scaling law found in coherent systems. We propose a range of constellation designs that allow us to separate the users' signals at the receiver by relying only on the knowledge of the average received power per user. We analyse the error probability and provide insights into the beneficial selection of the constellation parameters. Finally, we provide some numerical results showing that our proposals require a lower number of receive antennas to achieve a given error probability than other non-coherent benchmark schemes available in the literature, while they are not far from an equivalent coherent system relying on realistic channel estimation settings.

Unary Error Correction (UEC) codes constitute a recently proposed Joint Source and Channel Code (JSCC) family, conceived for alphabets having an infinite cardinality, whilst out-performing previously used Separate Source and Channel Codes (SSCCs). UEC based schemes rely on an iterative decoding process, which involves three decoding blocks when concatenated with a turbo code. Owing to this, following the activation of one of the three blocks, the next block to be activated must be chosen from the other two decoding block options. Furthermore, the UEC decoder offers a number of decoding options, allowing its complexity and error correction capability to be dynamically adjusted. It has been shown that iterative decoding convergence can be expedited by activating the specific decoding option that offers the highest Mutual Information (MI) improvement to computational complexity ratio. This paper introduces an iterative demodulator, which is shown to improve the associated error correction performance, while reducing the overall iterative decoding complexity. The challenge is that the iterative demodulator has to forward its soft-information to the other two iterative decoding blocks, and hence the corresponding MI improvements cannot be compared on a like-for-like basis. Additionally, we also propose a method of eliminating the logarithmic calculations from the adaptive iterative decoding algorithm, hence further reducing its implementational complexity without impacting its error correcting performance.

Visible Light Communication (VLC) combined with advanced illumination has been expected to become an integral part of next generation heterogeneous networks at the time of writing, by inspiring further research interests. From both the Cell-Centric (CC) and the User-Centric (UC) perspectives, various VLC cell formations, ranging from fixed-shape regular cells with different Frequency Reuse (FR) patterns and merged cells employing advanced transmission scheme to amorphous user-specific cells are investigated. Furthermore, different Multi-User Scheduling (MUS) algorithms achieving Proportional Fairness (PF) are implemented according to different cell formations. By analysing some critical and unique characteristics of VLC, our simulation results demonstrate that, the proposed MUS algorithms are capable of providing a high aggregate throughput and achieving modest fairness with low complexity in most of the scenarios considered.

Advanced downlink (DL) decode-and-forward two-way relaying (DF-TWR) is developed for the sake of maximizing the attainable spectral efficiency. Network-coded phase-shift keying (NC-PSK) holds the potential of significantly increasing the data rate of the network, while adaptive modulation is a powerful technique of improving both the energy and spectral efficiency. Hence power-controlled adaptive NC-PSK has the potential of achieving performance enhancements over fading channels. Given this framework, based on the bit-error-ratio (BER) bounds developed, we derive the closed-form spectral efficiency for a continuous-rate, continuous-power adaptive NC-PSK scheme, where both the transmit power and the transmit rate are optimized subject to specific power- and BER- constraints. We then conceive and investigate a discrete-rate scheme relying on a pair of solutions proposed for maximizing the throughput of the network. Our simulation results reveal that the proposed schemes are capable of achieving a higher spectral efficiency than their fixed-power counterparts.

This paper addresses the problem of secure amplify-and-forward (AF) relaying for multiple-input multiple output (MIMO) relaying networks in the presence of multiple eavesdroppers. Assuming practical imperfect eavesdroppers' channel state information (ECSI), we propose a robust approach to optimize the relay AF matrix, subject to power constraint, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination while satisfying a set of secrecy constraints. The ECSI errors are assumed to fall within some predefined bounded sets. Since the resultant optimization problem is non-convex and semi-infinite, we transform it into a form constituted by the differences of convex functions (DC) using suitable matrix transformation techniques. Then an algorithmic solution with proven convergence is proposed by resorting to the penalty-DC algorithm (P-DCA). Experimental results show the security of the proposed transceiver design against eavesdropping and the robustness against the channel uncertainties.

Detecting spectrum holes and efficiently accessing them are the two basic functions that enable a cognitive radio (CR) to make use of the licensed spectrums of a primary radio (PR). In this paper, we consider a CR scheme, which opportunistically accesses a PR channel for communication between a pair of nodes based on the stop-and-wait hybrid automatic repeat request (SW-HARQ). Hence, it is referred to as the cognitive SW-HARQ (CSW-HARQ) arrangement. In our CSW-HARQ system, the PR channel is modelled as a two-state Markov chain having `On' and `Off' states. The CR may only access the PR channel in its `Off' state. In this paper, we analyze both the throughput and delay performance of the CSW-HARQ system, for which a range of closed-form formulas are derived that are also validated by simulation results. Our performance results show that both the activities of PR users and the reliability of the CR channel have a substantial impact on the achievable performance of the CR system.

In this study, the authors investigate the benefits of phase‐rotation‐assisted precoding (PRP) technique in spatial modulation (SM) systems, which are based on maximum free distance d min . First, a closed‐form solution of the maximum‐ d min PRP matrix is derived for the scenario of having two transmit antennas ( N t = 2). Moreover, two numerical methods are proposed for dealing with the case of N t > 2. The complexity of the proposed algorithms is presented. The authors simulation results show that the proposed PRP algorithms provide beneficial bit error ratio performance improvements compared with both the conventional SM and with the existing adaptive SM.

In echoing Varshney's seminal concept of jointly transmitting information and energy, we propose the concept of an energy pattern aided Simultaneous Wireless Information and Power Transfer (SWIPT) system, where in addition to power transfer, information is conveyed both by the specific Receive Antenna (RA) indices to which the power is delivered as well as by the particular intensity of the power assigned to that particular RA pattern. By embedding information into energy patterns rather than imposing it by modulating classic radio waveforms, our proposed solution is capable of operating both in an integrated receiver mode and in a power-split mode, while relying on a low-complexity two-stage non-coherent detection algorithm. Both our analysis and simulations show that our energy pattern aided SWIPT system exhibits a beneficial immunity to any potential performance degradation imposed by power-conversion. Moreover, the achievable rate versus power conversion trade-off bounds are characterized, demonstrating that our proposed energy pattern aided SWIPT system leads to a beneficial wireless information and power transfer convergence.

In this letter, we quantify the transmit diversity order of the SM system operating in a closed-loop scenario. Specifically, the SM system relying on Euclidean distance based antenna subset selection (EDAS) is considered and the achievable diversity gain is evaluated. Furthermore, the resultant trade-off between the achievable diversity gain and switching gain is studied. Simulation results confirm our theoretical results. Specifically, switching gain is studied. Simulation results confirm our theoretical results. Specifically, at a symbol error rate of about 10-4the signal-to-noise ratio gain achieved by EDAS is about 7 dBat a symbol error rate of about 10-4the signal-to-noise ratio gain achieved by EDAS is about 7 dB in case of 16-QAM and about 5 dB in case of 64-QAM.

Unlike a generic PSK/QAM detector, which may visit a constellation diagram only once, a depth-first Sphere Decoder (SD) has to re-visit the same constellation diagram multiple times. Therefore, in order to prevent the SD from repeating the detection operations, the Schnorr-Euchner search strategy of Schnorr and Euchner may be invoked for optimizing the nodes' search-order, where the ideal case is for the SD to visit the constellation nodes in a zigzag fashion. However, when the hard-decision Multiple-Symbol Differential Sphere Detection (MSDSD) of Lampe et al. is invoked for using multiple receive antennas NR≥ 1, the Schnorr-Euchner search strategy has to visit and sort all the MPSK constellation points. A similar situation is encountered for the soft-decision MSDSD of Pauli et al., when the a priori LLRs gleaned from the channel decoder are taken into account. In order to tackle these open problems, in this paper, we propose a correlation process for the hard-decision MSDSD of Lampe et al. and a reduced-complexity design for the soft-decision MSDSD of Pauli et al., so that the Schnorr-Euchner search strategy always opts for visiting the MPSK constellation points in a zigzag fashion. Our simulation results demonstrate that a substantial complexity reduction is achieved by our reduced-complexity design without imposing any performance loss. Explicitly, up to 88.7% complexity reduction is attained for MSDSD (Nw= 4) aided D16PSK. This complexity reduction is quite substantial, especially when the MSDSD is invoked several times during turbo detection. Furthermore, in order to offer an improved solution and a comprehensive study for the soft-decision MSDSD, we also propose to modify the output of the SD to harmonize its operation with the near-optimum Approx-Log-MAP. Then the important subject of coherent versus noncoherent is discussed in the context of coded systems, which suggests that MSDSD aided DPSK is an eminently suitable candidate for turbo detection assisted coded systems operating at high Doppler frequencies.

With the proliferation of smart-phones and tablet PCs, the data rates of wireless communications have been soaring. Hence, the need for power-efficient communications relying on low-complexity multiple-stream detectors has become more pressing than ever. As a remedy, in this paper we design low-complexity soft-input soft-output quantum-assisted multi-user detectors (QMUD), which may be conveniently incorporated into state-of-the-art iterative receivers. Our design relies on extrinsic information transfer charts. Our QMUDs are then employed in multi-carrier interleave-division multiple-access (MC-IDMA) systems, which are investigated in the context of different channel code rate and spreading factor pairs, whilst fixing the total bandwidth requirement. One of our QMUDs is found to operate within 0.5 dB of the classical maximum a posteriori probability MUD after three iterations between the MUD and the decoders, while requiring only half its complexity, at a BER of 10-5in the uplink of a rank-deficient MC-IDMA system relying on realistic imperfect channel estimation at the receiver, while supporting 14 users transmitting QPSK symbols.

In this paper, we propose bit-by-bit iterative decoding for expediting the convergence of Repeat Accumulate (RA) decoders. In a conventional RA decoder, the repeat and accumulate component decoders are operated iteratively, to facilitate near-capacity communication. However, whenever one decoder is activated, the other is kept idle. The outputs of the active component decoder are stored until its operation is completed, whereupon the outputs are forwarded to the other decoder all at once and the activation of the decoders is swapped. The proposed bit-by-bit RA decoder expedites this process by allowing both component decoders to operate simultaneously, continuously exchanging outputs without buffering. We present both EXtrinsic Information Transfer (EXIT) charts and Bit Error Ratio (BER) results, which demonstrate that the proposed bit-by-bit RA decoder requires fewer decoding iterations to converge, at the cost of a slightly increased complexity per decoding iteration. Overall, we demonstrate that in a range of practical scenarios, the proposed bit-by-bit RA offers gains of up to 0.86 dB, without imposing any additional decoding complexity and without requiring any additional transmission-energy, -bandwidth or -duration.

In multihop diversity (MHD) aided multihop links, the nodes are assumed to have buffers for temporarily storing their received packets for further transmission at instances of good channel quality. Since adaptive modulation is employed, the number of packets in each time slot (TS) is affected both by the channel quality and the buffer fullness. During each time-slot (TS), the criterion used for activating a specific hop is that of transmitting the highest number of packets. When more than one hop is capable of transmitting the same number of packets, the particular hop having the highest channel quality (reliability) is activated. Hence, we refer to this regime as the maximum throughput adaptive rate transmission (MTART) scheme. The bit error ratio (BER), the outage probability, the throughput as well as the bandwidth-efficiency of the MTART scheme is analyzed. Our results demonstrate that our MTART regime has the potential of significantly outperforming conventional adaptive modulation. Naturally, the BER is improved by the buffering scheme advocated at the cost of an increased delay. Hence, the distribution of the end-to-end packet delay will also be characterized.

In this contribution, we propose and study a bufferaided opportunistic routing (BOR) scheme, which combines the benefits of both opportunistic routing and multihop diversity (MHD) aided transmissions. It was conceived for a buffer-aided three-node network (B3NN) composed of a source node (SN), a buffer-aided relay node (RN) and a destination node (DN). In this network, there are three channels namely the SN-RN, RN-DN and SN-DN channels. The key motivation is that we are aiming for activating the specific channels requiring a recuded energy dissipation. In order to study this problem, a three-dimensional (3D) transmission activation probability space (TAPS) is proposed, which is divided into four regions representing each of the three channels plus an outage region. In a specific time slot (TS), the instantaneous channel fading values may be directly mapped to a specific point in this 3D channel space. The BOR scheme then relies on the position of this point to select the most appropriate channel for its transmission. Both the energy dissipation and the outage probability (OP) are investigated for transmission in this network. The results show that when the system is operated at a normalized throughput of 0.4 packet/TS, the energy dissipation was reduced by 24.8% to 77.6% compared to three different benchmark schemes. Alternatively, our technique is capable of reducing the OP by 89.6% when compared to conventional opportunistic routing. As in all buffer-aided system, the performance improved with the cost of higher packet delay, which is also studied.

The performance of space shift keying (SSK) is known to be dominated by the minimum Euclidean distance (MED) in the received SSK constellation. In this paper, we propose a method of enhancing the MED in the received SSK constellation and improving both the attainable performance and the power efficiency by means of symbol scaling at the transmitter. To this aim, we formulate a pair of optimization problems, one for maximizing the MED subject to a specific transmit power constraint and one for minimizing the transmit power subject to a MED threshold. As these problems are NP-hard, we re-formulate their optimization using semidefinite relaxations, which results in convex problem formulations that can be efficiently solved using standard approaches. Moreover, we design pre-scaling techniques for imperfect channel state information at the transmitter, where the existing approaches are inapplicable. Our results show that the proposed schemes substantially improve the power efficiency of SSK systems with respect to state-of-the-art techniques by offering an improved performance for specific transmit power requirements or, equivalently, a transmit power reduction for a given MED threshold.

Expressions are derived for the coverage probability and average rate of both multi-user multiple input multiple output (MU-MIMO) and single input multiple output (SIMO) systems in the context of a fractional frequency reuse (FFR) scheme. In particular, given a reuse region of 1/3 (FR3) and a reuse region of 1 (FR1) as well as a signal-to-interference-plus-noise-ratio (SINR) threshold Sth, which decides the user assignment to either the FR1 or FR3 regions, we theoretically show that: 1) the optimal choice of Sthwhich maximizes the coverage probability is Sth= T, where T is the target SINR required for ensuring adequate coverage, and 2) the optimal choice of Sthwhich maximizes the average rate is given by Sth= T', where T' is a function of the path loss exponent, the number of antennas and of the fading parameters. The impact of frequency domain correlation amongst the OFDM sub-bands allocated to the FR1 and FR3 cell-regions is analysed and it is shown that the presence of correlation reduces both the coverage probability and the average throughput of the FFR network. Furthermore, the performance of our FFR-aided MU-MIMO and SIMO systems is compared. Our analysis shows that the (2 × 2) MU-MIMO system achieves 22.5% higher rate than the (1 × 3) SIMO system and for lower target SINRs, the coverage probability of a (2 × 2) MU-MIMO system is comparable to a (1 × 3) SIMO system. Hence the former one may be preferred over the latter. Our simulation results closely match the analytical results.

Heterogeneous cellular networks (HCNs) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations, we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function and formulate an optimization problem. Then, we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, a distributed BP algorithm is proposed based on the network's factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations and mobile users, with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that 1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method; 2) the modified BP offers a good delay performance at low communication complexity; 3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations; and 4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

As a complementary extension of established Radio Frequency (RF) Wireless Local Area Networks (WLANs), Visible Light Communication (VLC) using commercially available Light-Emitting Diode (LED) transmitters offers a huge data rate potential in this license-free spectral domain, whilst simultaneously satisfying energy-efficient illumination demands. Various VLC cell formations, ranging from a regular cell-layout associated with different Frequency Reuse (FR) patterns to merged cells by employing advanced transmission scheme are investigated. Furthermore, a hybrid Down-Link (DL) offering full RF-coverage by a WLAN and additionally supported by the abundant spectral resources of a VLC network is studied. Cooperative Load Balancing (LB) achieving Proportional Fairness (PF) is implemented by using both centralized and distributed resource-allocation algorithms. The performance of this hybrid RF/VLC system is analysed both in terms of its throughput and fairness in diverse cell formation scenarios. Our simulation results demonstrate that, the VLC system advocated is capable of providing a high Area Spectral Efficiency (ASE) and our hybrid RF/VLC system achieves the highest throughput and the highest grade of fairness in most of the scenarios considered.

Hierarchical modulation (HM) is widely employed across the telecommunication industry. The potential application of the coded HM scheme in cooperative communications has drawn much interest. In this paper, a twin-relay-aided triple-layer cooperative communication system is proposed. The system amalgamates rate-1/2 TTCM, triple-layer HM-64QAM, and twin-layer SPM-16QAM schemes in the context of cooperative communications. We have optimized the entire system based on the HM ratio pair (R1, R2), the superposition modulation (SPM) weighting pair (α, β), and the positions of the two relays. The simulation results show that our optimized system is capable of reliably transmitting a triple-layer HM-64QAM signal with the aid of two time slots at an average signal-to-noise ratio of 6.94 dB per time slot.

Irregular joint source and channel coding (JSCC) scheme is proposed, which we refer to as the irregular unary error correction (IrUEC) code. This code operates on the basis of a single irregular trellis, instead of employing a set of separate regular trellises, as in previous irregular trellis-based codes. Our irregular trellis is designed with consideration of the UEC free distance, which we characterize for the first time in this paper. We conceive the serial concatenation of the proposed IrUEC code with an irregular unity rate code (IrURC) code and propose a new EXtrinsic Information Transfer (EXIT) chart matching algorithm for parametrizing these codes. This facilitates the creation of a narrow EXIT tunnel at a low Eb/N0value and provides near-capacity operation. Owing to this, our scheme is found to offer a low symbol error ratio (SER), which is within 0.4 dB of the discrete-input continuous-output memoryless channel (DCMC) capacity bound in a particular practical scenario, where gray-mapped quaternary phase shift keying (QPSK) modulation is employed for transmission over an uncorrelated narrowband Rayleigh-fading channel with an effective throughput of 0.508bits-1Hz-1. Furthermore, the proposed IrUEC-IrURC scheme offers a SER performance gain of 0.8 dB, compared to the best of several regular and irregular separate source and channel coding (SSCC) benchmarkers, which is achieved without any increase in transmission energy, bandwidth, transmit duration, or decoding complexity.

We consider a cognitive radio (CR) network consisting of a secondary transmitter (ST), a secondary destination (SD) and multiple secondary relays (SRs) in the presence of an eavesdropper, where the ST transmits to the SD with the assistance of SRs, while the eavesdropper attempts to intercept the secondary transmission. We rely on careful relay selection for protecting the ST-SD transmission against the eavesdropper with the aid of both single-relay and multi-relay selection. To be specific, only the “best” SR is chosen in the single-relay selection for assisting the secondary transmission, whereas the multi-relay selection invokes multiple SRs for simultaneously forwarding the ST's transmission to the SD. We analyze both the intercept probability and outage probability of the proposed single-relay and multi-relay selection schemes for the secondary transmission relying on realistic spectrum sensing. We also evaluate the performance of classic direct transmission and artificial noise based methods for the purpose of comparison with the proposed relay selection schemes. It is shown that as the intercept probability requirement is relaxed, the outage performance of the direct transmission, the artificial noise based and the relay selection schemes improves, and vice versa. This implies a trade-off between the security and reliability of the secondary transmission in the presence of eavesdropping attacks, which is referred to as the security-reliability trade-off (SRT). Furthermore, we demonstrate that the SRTs of the single-relay and multi-relay selection schemes are generally better than that of classic direct transmission, explicitly demonstrating the advantage of the proposed relay selection in terms of protecting the secondary transmissions against eavesdropping attacks. Moreover, as the number of SRs increases, the SRTs of the proposed single-relay and multi-relay selection approaches significantly improve. Finally, our numerical results show that as expected, the multi-relay selection scheme achieves a better SRT performance than the single-relay selection.

The resource-allocation (RA) problems of mobile terminals (MTs) are investigated in a heterogeneous wireless network, where both a visible light communication system and an RF femtocell system are deployed in a room. We consider diverse quality-of-service requirements in terms of the data rate, fairness, and statistical delay requirements. Inspired by the concept of effective capacity, we formulate our optimization problems applying α-proportional fairness while satisfying specific statistical delay constraints. Two types of MTs, namely, multihoming MTs and multimode MTs, are considered, where multihoming MTs have the capability of aggregating resources from different networks, whereas the multimode MTs always select a single network for their connection. Our optimization procedure solves the RA probability problem for multihoming MTs with the aid of a decentralized algorithm. By contrast, our optimization problem involves both network selection and RA probability optimization for multimode MTs, which may be regarded as a mixed-integer nonlinear problem. Since this problem is computationally intractable, a suboptimal decentralized method is proposed for solving it. Simulation results are also presented for clarifying the performance of the proposed algorithm. It is shown that the multimode MTs are capable of achieving similar performance to that of the multihoming MTs when the statistical delay requirements are loose. However, as expected, the multihoming MTs attain a better performance when we tighten the delay requirements.

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input-multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains while dispensing with the idealized simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximum-likelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process.

The recently proposed multiple input multiple output (MIMO) transmission scheme termed as generalized pre-coding aided spatial modulation (GPSM) is analyzed, where the key idea is that a particular subset of receive antennas is activated and the specific activation pattern itself conveys useful implicit information. We provide the upper bound of both the symbol error ratio (SER) and bit error ratio (BER) expression of the GPSM scheme of a low-complexity decoupled detector. Furthermore, the corresponding discrete-input continuous-output memoryless channel (DCMC) capacity as well as the achievable rate is quantified. Our analytical SER and BER upper bound expressions are confirmed to be tight by our numerical results. We also show that our GPSM scheme constitutes a flexible MIMO arrangement and there is always a beneficial configuration for our GPSM scheme that offers the same bandwidth efficiency as that of its conventional MIMO counterpart at a lower signal to noise ratio (SNR) per bit.

The potentially excessive complexity of the Maximum Likelihood Multi-User Detector (ML MUD) in large-scale Spatial Division Multiple Access (SDMA) systems dictates the employment of low-complexity sub-optimal MUDs in the context of conventional systems. However, this limitation was circumvented by the recently proposed Dürr-Høyer Algorithm (DHA)-aided Quantum Weighted Sum Algorithm (QWSA)-based Quantum Multi-User Detector (QMUD) employed for performing optimal ML iterative detection in SDMA systems. Focusing our attention on the QWSA, we analyse the QMUD and the evolution of the quantum system with the aid of a simple SDMA uplink scenario. We characterize the performance of the DHA-QWSA QMUD advocated, which is capable of matching the performance of the ML MUD both in terms of its EXIT charts and BER curves.

In multihop diversity aided multihop links, the number of bits transmitted in each Time Slot (TS) is affected by both the Channel Quality (CQ) and the Buffer Fullness (BF), when adaptive modulation is employed. We assume that every node has buffers for temporarily storing its received packets for further transmission at instances of good CQ. For the sake of improving the throughput, a Maximum Throughput Adaptive Rate Transmission (MTART) scheme was proposed, where the specific hop having the capability of transmitting the highest number of bits (packets) will be activated. If more than one hops are capable of transmitting the same number of bits, the particular hop having the highest CQ (reliability) is activated. We demonstrate that the MTART scheme has 8 dB gain at the Outage Probability (OP) of 10-3and has 3 dB gain in terms of the throughput attained in comparison to the conventional adaptive modulation aided scheme.

We propose a simple yet effective norm-based joint transmit and receive antenna selection (NBJTRAS) assisted and two-tier channel estimation (TTCE) aided space-time shift keying (STSK) system, which is capable of significantly outperforming the conventional STSK system, while efficiently utilising available radio frequency (RF) chains. Specifically, the NBJTRAS carries out antenna selection based on the channel estimation (CE) generated using a low-complexity training based least square channel estimator by reusing RF chains. The selected sub-channel matrix is further refined by an efficient semi-blind CE and data detection scheme. Our simulation results show that only a few iterations are sufficient for the TTCE scheme to approach the optimal maximum-likelihood detection performance associated with perfectly channel state information.

Asymmetric Distributed Source-Channel coding (DSC) is considered, where a pair of correlated sources are transmitting to a central node. The distributed scheme is based on Turbo Trellis Coded Modulation (TTCM), where the first source will be channel encoded and then compressed before it is sent over Nakagami-m fading channels. The second source signal, however, is assumed to be available flawlessly at the destination for exploitation as side information for improving the decoding performance of the first source. A wide range of fading scenarios were considered, where reliable communications approaching the Slepian-Wolf Shannon (SW/S) limit were exhibited. Finally, the scheme is capable of adapting to the time-variant short-term correlation between the two sources.

Radio over Fiber (RoF) distribution aided co-operation of Remote Access Points (RAPs) is proposed for jointly transmitting data to the users in the downlink (DL) of a Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. Joint transmission is performed with the aid of Block Diagonalization (BD), where the transmitted signal is pre-distorted in order to overcome the non-linearity imposed by the optical modulator. We demonstrate that with adequate design, the users can obtain high data rates with very small degradation introduced by the RoF transmission. Quantitatively, when M=7 RAPs, each equipped with t=2 transmit antennas (TAs) each, cooperate to serve N=7 simultaneous users, also equipped with r=2 receive antennas (RAs), the SNR degradation is kept below 0.1 dB compared to a system assuming a perfect RoF channel. On the other hand, the SNR performance degradation is around 1.2 dB, when the number of antennas at the transmitters and the receivers is increased to t=r=8.

Quantum-based transmission is an attractive solution conceived for achieving absolute security. In this quest, we have conceived an EXtrinsic Information Transfer (EXIT) chart aided channel code design for symbol-based entanglement-assisted classical communication over quantum depolarizing channels. Our proposed concatenated code design incorporates a Convolutional Code (CC), a symbol-based Unity Rate Code (URC) and a soft-decision aided 2-qubit Superdense Code (2SD), which is hence referred to as a CC-URC-2SD arrangement. We have optimized our design with the aid of non-binary EXIT charts. Our proposed design operates within 1 dB of the achievable capacity, providing attractive performance gains over its bit-based counterpart. Quantitatively, the bit-based scheme requires 60% more iterations than our symbol-based scheme for the sake of achieving perfect decoding convergence. Furthermore, we demonstrate that the decoding complexity can be reduced by using memory-2 and memory-3 convolutional codes, while still outperforming the bit-based approach.

In this paper buffer-aided Opportunistic Routing (OR) was designed with the aid of the novel concept of linear Transmission Activation Probability Space (TAPS) partitioning invoked for relay-assisted networks, which combines the benefits of both OR [1] and of buffer-aided transmissions [2]. More specifically, a packet may be transmitted from the Source Node (SN) to the Destination Node (DN) either directly or indirectly via one of theMRelay Nodes (RNs), depending on the instantaneous channel qualities. The above-mentioned linear multi-dimensional TAPS partitioning concept is proposed for partitioning the transmission space into (2M+1) transmission regions plus an outage region, while ensuring that the number of input packets is equal to the number of output packets at each RN's buffer. The benefit of having a buffer and tolerating the associated delay is that the best channel is activated for transmission based on our linear TAPS partitioning method.

A pragmatic distributed algorithm (PDA) is proposed for supporting the efficient spectral access of multiple Primary Users (PUs) and Cognitive Users (CUs) in cooperative Cognitive Radio (CR) networks. The CUs may serve as relay nodes for relaying the signal received from the PUs to their destinations, while both the PUs' and the CUs' minimum rate requirements are satisfied. The key idea of our PDA is that the PUs negotiate with the CUs concerning the specific amount of relaying and transmission time, whilst reducing the required transmission power or increasing the transmission rate of the PU. Our results show that the cooperative spectral access based on our PDA reaches an equilibrium, when it is repeated for a sufficiently long duration. These benefits are achieved, because the PUs are motivated to cooperate by the incentive of achieving a higher PU rate, whilst non-cooperation can be discouraged with the aid of a limited-duration punishment.

A novel adaptive turbo-coded soft-decision aided differential detection (ATSDD) scheme is proposed for cooperative uplink wireless and Radio-over-fiber (ROF) transmission in a Fractional Frequency Reuse (FFR) based multicell, multiuser system. More specifically, the ATSDD scheme is employed by the Mobile Station (MS) for reliably conveying the source bits to a pair of Remote Antennas (RAs) by appropriately adjusting the modulation mode according to the near-instantaneous wireless and ROF channel conditions. The ATSDD switching thresholds are specifically adjusted for ensuring that the Bit Error Ratio (BER) remains below 10^(-5). We also investigated the effect of phase-rotations, which is routinely inflicted by practical imperfect ROF links. We demonstrate that our ATSDD scheme increases the overall throughput.

A buffer-aided two hop link is studied, where the RN is capable of temporarily storing the received packets. We commerce by defining the concept of a two-dimensional Channel Probability Space (CPS) based on the source-relay and relay-destination channel. Specifically, a non-linear CPS division method is proposed, which partitions the CPS into several regions representing the quality of the specific channels plus an outage region. Then the best channel is activated for the sake of minimizing the system's energy dissipation. Finally, the proposed buffer-aided transmission scheme relying on our non-linear CPS division regime is investigated and the results show that at given average end-to-end energy dissipation, the outage probability was reduced by 33.5% compared to the benchmark scheme.

Since mobile communications exhibit strong social characteristics, based on the potential common interests of mobile users, mobile social networks (MSNs) are capable of mitigating the tele-traffic bottleneck. By multicasting the content of common interest from a content owner to content seekers within the owner's transmission range, a distributed MSNs architecture is proposed, which is capable of mitigating the tele-traffic imposed on network operators. In this contribution, the social relationship between a pair of MSN users is defined according to their geographic characteristics. By jointly considering the geographic social relationships and the wireless propagation environment, we derive the closed-form equations for evaluating both the throughput and delay of the social unicast/multicast transmissions. Simulation results are provided, both for supporting our theoretical analysis, as well as for investigating the impact of social relationships on the achievable network performance. Based on the results presented, we conclude that a more socially-minded content owner is particularly efficient in multicasting the content of common interest to content seekers.

A novel hybrid information dissemination scheme is proposed for picocellular systems. At the first stage of our scheme, some of the mobile users (MUs) successfully receive the information of common interest via BS-aided multicast. At the second stage, the information is cooperatively multicast (co-multicast) by the information owners (IOs) in a self-organized ad hoc network until all the MUs receive it. Since limited resources are provided for the second stage of the spontaneous co-multicast, some of the IOs are appointed for relaying the information to the hitherto unserved MUs. Several relay selection protocols are conceived for the sake of improving the performance of our hybrid information dissemination scheme. The simulation results demonstrate that our scheme may reduce the average dissemination delay and the average energy dissipation by 20% and 70%, respectively, when compared to the conventional BS aided single-hop multicast. Furthermore, we demonstrate that relying on the IOs associated with the best links plays a crucial role in facilitating spontaneous information dissemination.

The authors consider the channel estimation problem in the context of a linear equaliser designed for a frequency selective channel, which relies on the minimum bit‐error‐ratio (MBER) optimisation framework. Previous literature has shown that the MBER‐based signal detection may outperform its minimum‐mean‐square‐error (MMSE) counterpart in the bit‐error‐ratio performance sense. In this study, they develop a framework for channel estimation by first discretising the parameter space and then posing it as a detection problem. Explicitly, the MBER cost function (CF) is derived and its performance studied, when transmitting binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. It is demonstrated that the MBER based CF aided scheme is capable of outperforming existing MMSE, least square‐based solutions.

The performance of high‐speed railway wireless communication systems is studied in the presence of co‐channel interference and imperfect channel estimation in the uplink. The authors derive exact closed‐form expressions for the outage probability and investigate the impact of fading severity. New explicit expressions are derived for both the level crossing rate and average outage duration for illustrating the impact of mobile speed and channel estimation errors on the achievable system performance. Our results are generalised and hence they subsume a range of previously reported results.

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multi-level K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses M^K CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-H\o yer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems.

A design methodology based on the Minimum Bit Error Ratio (MBER) framework is proposed for a non-regenerative Multiple-Input Multiple-Output (MIMO) relay-aided system to determine various linear parameters. We consider both the Relay-Destination (RD) as well as the Source-Relay-Destination (SRD) link design based on this MBER framework, including the precoder, the Amplify-and-Forward (AF) matrix and the equalizer matrix of our system. It has been shown in the previous literature that MBER based communication systems are capable of reducing the Bit-Error-Ratio (BER) compared to their Linear Minimum Mean Square Error (LMMSE) based counterparts. We design a novel relay-aided system using various signal constellations, ranging from QPSK to the general M-QAM and M-PSK constellations. Finally, we propose its sub-optimal versions for reducing the computational complexity imposed. Our simulation results demonstrate that the proposed scheme indeed achieves a significant BER reduction over the existing LMMSE scheme.

A pragmatic distributed algorithm (PDA) is proposed for supporting the efficient spectral access of multiple Primary Users (PUs) and Cognitive Users (CUs) in cooperative Cognitive Radio (CR) networks. The novelty of our PDA is that the PUs negotiate with the CUs concerning the specific amount of relaying and transmission time, the CU is granted, which the CU will either accept or decline. The CUs may serve as relay nodes for relaying the signal received from the PUs to their destinations, while both the PUs' and the CUs' minimum rate requirements are satisfied. This will reduce the required transmission power and/or increase the transmission rate of the PU. Our results show that the proposed scheme performs better than the benchmarker, despite its significantly lower overhead and complexity. Moreover, we show that the cooperative spectral access based on our PDA reaches an equilibrium, when it is repeated for a sufficiently long duration. These benefits are achieved, because the PUs are motivated to cooperate by the incentive of achieving a higher PU rate, whilst non-cooperation can be discouraged with the aid of a limited-duration punishment. Furthermore, we invoke an attractive practical adaptive Turbo Trellis Coded Modulation (ATTCM) scheme, which appropriately adjusts the code rate and the modulation mode according to the near-instantaneous channel conditions. It was found that the joint design of coding, modulation and user-cooperation may lead to significant mutual benefits for all the PUs and the CUs.

Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-Output scheme that jointly uses antenna indices and a conventional signal set to convey information. It has been shown that the Maximum-Likelihood (ML) detector of an SM system involves joint detection of the transmit antenna index and of the transmitted symbol, hence, the ML search complexity grows linearly with the number of transmit antennas and the size of the signal set. To circumvent the problem, we show that the ML search complexity of an SM system may be rendered independent of the constellation size, provided that the signal set employed is a square- or a rectangular-QAM. Furthermore, we derive bounds for the capacity of the SM system and derive the optimal power allocation between the data and the training sequences by maximizing the worst-case capacity bound of the SM system operating with imperfect channel state information. We show, with the aid of our simulation results, that the proposed detector is ML-optimal, despite its lowest complexity amongst the existing detectors. Furthermore, we show that employing the proposed optimal power allocation provides a substantial gain in terms of the SM system's capacity as well as signal-to-noise ratio compared to its equal-power-allocation counterpart. Finally, we compare the performance of the SM system to that of the conventional Multiple-Input Multiple-Output (MIMO) system and show that the SM system is capable of outperforming the conventional MIMO system by a significant margin, when both the systems are employing optimal power splitting.

In this paper we propose a novel low-complexity Joint Source and Channel Code (JSCC), which we refer to as the Elias Gamma Error Correction (EGEC) code. Like the recently-proposed Unary Error Correction (UEC) code, this facilitates the practical near-capacity transmission of symbol values that are randomly selected from a set having an infinite cardinality, such as the set of all positive integers. However, in contrast to the UEC code, our EGEC code is a universal code, facilitating the transmission of symbol values that are randomly selected using any monotonic probability distribution. When the source symbols obey a particular zeta probability distribution, our EGEC scheme is shown to offer a 3.4 dB gain over a UEC benchmarker, when Quaternary Phase Shift Keying (QPSK) modulation is employed for transmission over an uncorrelated narrowband Rayleigh fading channel. In the case of another zeta probability distribution, our EGEC scheme offers a 1.9 dB gain over a Separate Source and Channel Coding (SSCC) benchmarker.

Network coded modulation (NCM) holds the promise of significantly improving the efficiency of two-way wireless relaying. In this contribution, we propose near instantaneously adaptive variable-rate, variable-power QAM/PSK for NC-aided decode-and-forward two-way relaying (DF-TWR) to maximize the average throughput. The proposed scheme is optimized subject to both average-power and bit-error-ratio (BER) constraints. Based on the BER bounds, we investigate a discrete-rate adaptation scheme, relying on a pair of solutions proposed for maximizing the spectral efficiency of the network. We then derive a closed-form solution based power adaptation policy for a continuous-rate scheme and quantify the signal-to-noise ratio (SNR) loss imposed by NC-QAM. Our simulation results demonstrate that the proposed discrete adaptive NC-QAM/PSK schemes are capable of attaining a higher spectral efficiency than their fixed-power counterparts.

Most of the nodes in ad hoc networks rely on batteries, which requires energy saving. Hence, numerous energy-efficient routing algorithms have been proposed for solving this problem. In this paper, we exploit the benefits of cross-layer information exchange, such as the knowledge of the Frame Error Rate (FER) in the physical layer, the maximum number of retransmissions in the Medium Access Control (MAC) layer and the number of relays in the network layer. Energy-consumption-based Objective Functions (OF) are invoked for calculating the end-to-end energy consumption of each potentially available route for both Traditional Routing (TR) and for our novel Opportunistic Routing (OR), respectively. We also improve the TR and the OR with the aid of efficient Power Allocation (PA) for further reducing the energy consumption. For the TR, we take into account the dependencies amongst the links of a multi-hop route, which facilitates a more accurate performance evaluation than upon assuming the links that are independent. Moreover, two energy-efficient routing algorithms are designed based on Dijkstra's algorithm. The algorithms based on the energy OF provide the theoretical bounds, which are shown to be close to the bound found from exhaustive search, despite the significantly reduced complexity of the former. Finally, the end-to-end throughput and the end-to-end delay of this system are analyzed theoretically and a new technique of characterizing the delay distribution of OR is proposed. The simulation results show that our energy-efficient OR outperforms the TR and that their theoretical analysis accurately matches the simulation results.

A joint source-channel coding has attracted substantial attention with the aim of further exploiting the residual correlation residing in the encoded video signals for the sake of improving the reconstructed video quality. In our previous paper, a first-order Markov process model was utilized as an error concealment tool for exploiting the intra-frame correlation residing in the Wyner-Ziv (WZ) frame in the context of pixel-domain distributed video coding. In this contribution, we exploit the interview correlation with the aid of an interview motion search in distributed multi-view video coding (DMVC). Initially, we rely on the system architecture of WZ coding invoked for multiview video. Then, we construct a novel mesh-structured pixel-correlation model from the inter-view motion vectors and derive its decoding rules for joint source-channel decoding. Finally, we benchmark the attainable system performance against the existing pixel-domain WZ coding based DMVC scheme, where the classic turbo codec is employed. Our simulation results show that substantial bitrate reductions are achieved by employing the proposed motion-aware mesh-structured correlation modelling technique in a DMVC scheme.

Layered video coding is capable of progressively refining the reconstructed video quality with the aid of multiple layers of unequal importance. When the base layer (BL) is corrupted or lost due to channel impairments, the enhancement layers (ELs) must be discarded by the video decoder, regardless whether they are perfectly decoded or not, which implies that the transmission power assigned to the ELs is wasted. To circumvent this problem, we proposed a bit-level inter-layer forward error correction (IL-FEC) scheme for layered video transmission in our previous work, which implanted the systematic bits of the BL into the systematic bits of the ELs using exclusive-OR operations (XOR). This allowed the receiver to exploit the implanted bits of the ELs for assisting the BL's decoding and hence improved the overall system performance of our IL-FEC aided layered video scheme. In this treatise, we find the specific FEC coding rates in a real-time on-line fashion for the sake optimizing the overall system performance. The proposed procedure is widely applicable to diverse wireless transceivers and FEC codecs. Our simulation results show that the proposed optimized IL-FEC system outperforms the traditional optimal UEP by about 1.9 dB of Eb/N0at a peak signal-to-noise ratio (PSNR) of 38 dB. Viewing the improvements in terms of the video quality, 3.3 dB of PSNR improvement is attained at an Eb/N0of 10 dB, when employing a recursive systematic convolutional (RSC) code.

We first conceive a novel transmission protocol for a multi-relay multiple-input-multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. We then address the associated network-wide spectral efficiency (SE) and energy spectral efficiency (ESE) optimization problems. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input-single-output (MISO) channels, which are essentially spatially multiplexed for transmission. Hence, these effective MISO channels are referred to as spatial multiplexing components (SMCs). For the sake of improving the SE/ESE performance attained, the SMCs are grouped using a pair of proposed grouping algorithms. The first is optimal in the sense that it exhaustively evaluates all the possible combinations of SMCs satisfying both the semi-orthogonality criterion and other relevant system constraints, whereas the second is a lower-complexity alternative. Corresponding to each of the two grouping algorithms, the pair of SE and ESE maximization problems are formulated, thus the optimal SMC groups and optimal power control variables can be obtained for each subcarrier block. These optimization problems are proven to be concave, and the dual decomposition approach is employed for obtaining their solutions. Relying on these optimization solutions, the impact of various system parameters on both the attainable SE and ESE is characterized. In particular, we demonstrate that under certain conditions the lower-complexity SMC grouping algorithm achieves 90% of the SE/ESE attained by the exhaustive-search based optimal grouping algorithm, while imposing as little as 3.5% of the latter scheme's computational complexity.

Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.

This contribution presents a method of obtaining the optimal power and subcarrier allocations that maximize the energy-efficiency (EE) of a multi-user, multi-relay, orthogonal frequency division multiple access (OFDMA) cellular network. Initially, the objective function (OF) is formulated as the ratio of the spectral-efficiency (SE) over the power consumption of the network. This OF is shown to be quasi-concave, thus Dinkelbach's method can be employed for solving it as a series of parameterized concave problems. We characterize the performance of the aforementioned method by comparing the optimal solutions obtained to those found using an exhaustive search. Additionally, we explore the relationship between the achievable SE and EE in the cellular network upon increasing the number of active users. In general, increasing the number of users supported by the system benefits both the SE and EE, and higher SE values may be obtained at the cost of EE, when an increased power may be allocated.

In our previous work, it was shown that the conventional approximate Bayes' theorem based probabilistic data association (PDA) algorithms output “nominal APPs”, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, in this paper we propose an exact Bayes' theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using M-ary modulation. Furthermore, we demonstrate that introducing inner iterations into EB-Log-PDA, which is common practice in conventional-PDA aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we show that the EB-Log-PDA based IDD scheme operating without any inner PDA iterations has a similar performance to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.

An iterative Joint Source and Turbo Trellis Coded Hierarchical Modulation is introduced for robust context-aware medical image transmission. Lossless source compression as well as Quality of Service (QoS) might be considered as the main constraints in the telemedicine field. Our proposed scheme advocated was design to exploit both the joint source-and-channel iterative decoding and the cooperative structure in order for tackling these requirements. The Source Node (SN) is constituted by a lossless Variable Length Code (VLC) and Turbo Trellis-Coded Modulation (TTCM) which relies on Hierarchical Modulation (HM). The Relay Node (RN) is used to support the transmission of the most important content of the image. Our proposed scheme exhibits a robustness performance over a realistic uncorrelated Rayleigh fading channel, while it outperforms the non-cooperative scheme by 3 dB at asymptotic (error-free) Peak Signal to Noise Ratio (PSNR) value.

An iterative Joint Source and Channel Coded Modulation (JSCCM) scheme is proposed for robust video transmission over two-way relaying channels. The system advocated was designed for improving the throughput, reliability and coverage area compared to that of conventional one-way relaying schemes. We consider a two-user communication system, where the users exchange their information with the aid of a twin-antenna Relay Node (RN). For each user the proposed lossless video scheme is comprised of a Variable Length Code (VLC) encoder and two Turbo Trellis Coded Modulation (TTCM) encoders one at the Source Node (SN) and one at the RN. The spatio-temporal redundancy of the video sequence is exploited for reducing the iterative decoding complexity. The decoding convergence behaviour of the decoder as well as the power sharing ratio between the two SNs and the RN are characterized with the aid of EXtrinsic Information Transfer (EXIT) charts. Our proposed scheme exhibits an SNR gain of 9 dB compared to the non-cooperative scheme, when communicating over Rayleigh fading channels.

In layered video streaming, the enhancement layers (ELs)must be discarded by the video decoder, when the base layer (BL) is corrupted or lost due to channel impairments. This implies that the transmit power assigned to the ELs is wasted, when the BL is corrupted. To combat this effect, in this treatise we investigate the inter-layer turbo (IL-turbo) code, where the systematic bits of the BL are implanted into the systematic bits of the ELs at the transmitter. At the receiver, when the BL cannot be successfully decoded, the information of the ELs may be utilized by the IL-turbo decoder for the sake of assisting in decoding the BL. Moreover, for providing further insights into the IL technique the benefits of the IL-turbo scheme are analyzed using extrinsic information transfer (EXIT) charts in the scenario of unequal error protection (UEP) coded layered video transmission. Finally, our data partitioning based experiments show that the proposed scheme outperforms the traditional turbo code based UEP scheme by about an Eb=N0 of 1.1 dB at a peak signal-to-noise ratio (PSNR) of 36 dB or 3 dB of PSNR at an Eb=N0 of -5.5 dB at the cost of a complexity increase of 13%.

We propose a novel reduced-complexity detector for the orthogonal frequency division multiple access (OFDMA)/single-carrier frequency division multiple access (SC-FDMA)-aided space-time shift keying (STSK) architecture. STSK employing OFDMA/SC-FDMA has recently been shown to be beneficial in dispersive multiuser downlink/uplink scenarios. These schemes exhibit excellent performance at a considerably reduced decoding complexity. In this paper, we propose a new detector, which is capable of further reducing the decoding complexity. The proposed detector is particularly suitable for STSK-based transmission over frequency-selective multiple-input multiple-output (MIMO) channels employing frequency-domain equalization (FDE). The complexity of the proposed scheme is quantified and it is observed that the scheme maintains its superior performance at a significantly reduced complexity.