Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.

Fluid antenna systems (FAS) have emerged as a promising technology to achieve high spatial diversity by dynamically reconfiguring multiple closely spacedNantenna ports. However, the inherent spatial correlation among these ports poses significant challenges for accurate performance analysis. Traditional block-correlation modeling algorithms, which partition theN×NToeplitz-structured correlation matrix into independentDblocks with constant correlation coefficients, often yield substantial approximation errors to block-correlation models, especially in scenarios with limited ports. In this paper, we revisit the spatial block-correlation model for FAS and introduce a novel block-correlation modeling algorithm in tuning the model parameters, which realizes the variable block-correlation model in practice. Our proposed approach derives closed-form expressions for the optimal block-specific correlation coefficients and develops a low-complexity heuristic algorithm that reduces the computational complexity from exponentialDN–Dto linear (N–D) ×Dsearches,thereby achieving significantly lower approximation error compared to constant correlation models. To validate the effectiveness of our variable block-correlation modeling algorithm, we first apply it to point-to-point FAS communications with closely spaced ports, deriving analytical expressions for the joint probability density function (PDF) of channel amplitudes and outage probability. Our analysis shows that the proposed algorithm offers tractable performance evaluation and superior accuracy, particularly when the number of ports is small (NThese results underscore the practical value of our approach for the design and optimization of next-generation FAS-based wireless networks.

In this paper, we utilize frequency hopping spread spectrum (FHSS) to help enhance covert performance in frequency diverse array (FDA) covert system. Compared to conventional FDA covert communication, FHSS signals can hide in different channels by rapidly switching the transmission frequency to avoid being detected. Therefore, FHSS-aided FDA covert system can effectively counter the warden with strong detection ability and enhance covert performance. We focus on maximizing the covert capacity during the entire transmission by jointly optimizing the beamfoming vector and antenna frequency vector at the transmitter. Based on the system equipment constraints, two different optimization schemes, i.e., instantaneous optimization scheme (IOS) and robust optimization scheme (ROS), are proposed to solve the problem. Moreover, to evaluate the covert performance provided by FHSS-aided FDA covert communication, we introduce three benchmark schemes, i.e., phased array (PA) and logarithmic distribution FDA (Log-FDA) and non-FHSS. Simulation results demonstrate that FHSS combined with IOS can significantly improve covert performance compared to other benchmark schemes. In contrast, FHSS combined with ROS can effectively enhance covert performance when transmitting signals over a wide bandwidth.

The beam focusing characteristic of frequency diverse array (FDA) in direction-distance dimension has been widely applied in covert communication. To reduce the covert performance loss caused by time-varying FDA channels, the instantaneous optimization of antenna beamforming vector (ABV) and antenna frequency vector (AFV) is required for FDA. However, due to the difficulty in acquiring instantaneous channel state information (CSI) and the limitations of transmit device, instantaneous optimization scheme (IOS) may not be feasible in practical communication scenarios. Moreover, the existing robust schemes only adopt some simple and fixed AFVs without optimizing AFV. This paper investigates robust optimization scheme (ROS) in FDA-aided covert system, where the optimization frequency of the ABV and AFV at the transmitter can be adaptively adjusted based on the acquired CSIs. We maximize the average covert rate during the transmission by jointly optimizing ABV and AFV, and modify the conventional block successive upper-bound minimization (BSUM) method to solve the optimization problem. Furthermore, both perfect CSI and partial CSI at the warden are considered. Numerical results demonstrate that our proposed ROS outperforms the existing robust schemes, especially in the cases of highly correlated channels and partial CSI. Although ROS is indeed inferior to IOS, enhancing the channel decoupling capability of FDA and deteriorating the detection capability of the warden can effectively narrow the covert performance gap between ROS and IOS. Moreover, the beampattern generated by the optimal ABV and AFV at each time slot ensures that the beamfoming gain is maximized at the legitimate receiver and minimized at the warden simultaneously.

The sixth-generation (6G) wireless systems aim to achieve ultra-high data rates and enhanced connectivity, driven by the increasing demand for broadband services and interactive applications. However, achieving such high data rates introduces significant challenges, such as intersymbol interference (ISI), which degrades signal quality and system performance. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted single-carrier (SC) frequency domain equalization (FDE) to mitigate ISI and enhance broadband connectivity. The RIS reflection coefficients are configured to maximize the received signal power at the user equipment (UE) by compensating for the phase of the dominant-tap in the end-to-end channel of the proposed system. This configuration enables coherent signal combining at the receiver, thereby enhancing signal quality and overall system performance. The performance of the proposed system is analyzed over frequency-selective Rayleigh fading channels, and the pairwise error probability (PEP) expression and upper bound for the bit error rate (BER) are derived. Analytical and simulation results demonstrate that the proposed framework consistently outperforms the state-of-the-art cyclic prefix (CP) SC-RIS system, achieving up to two orders of magnitude improvement in terms of BER. Moreover, diversity analysis shows that while conventional CP transmission achieves a diversity order of 1, the proposed RIS-assisted framework attains a higher order equal tokλ, which corresponds to the shape parameter of the distribution of the weighted sum power of the end-to-end channel. This parameter scales linearly with both the number of RIS elements and the number of effective channel taps, thereby enabling enhanced diversity in the presence of richer multipath propagation and larger RIS arrays. Finally, performance analysis explicitly demonstrates that increasing the number of RIS elements and channel taps further improves system performance, highlighting the advantages of RIS-aided spatial configuration and multipath diversity exploitation.

FAS-enabled cooperative non-orthogonal multiple access (FAS-CoNOMA) systems capture the potential of fluid antenna systems in enhancing network performance. In this system, a base station (BS) transmits a superposition signal to a central user (CU) and a cell-edge user (EU), both equipped with FAS. Specifically, the CU decodes the signal intended for the EU and cooperatively relays it to improve the EU’s communication performance. The EU employs selective combining (SC) or maximum ratio combining (MRC) to receive signals from both the BS and CU. By leveraging the dynamic properties of FAS to improve user differentiation, the CoNOMA system effectively enhances network performance compared to traditional NOMA, OMA, and fixed position antenna (FPA) systems. To address the challenging spatial correlation properties in FAS, we utilize the block-diagonal matrix approximation (BDMA) model to calculate the outage probabilities for both the CU and EU. We then derive upper bound, lower bound, and asymptotic approximation of the outage probabilities to gain deeper insights. Furthermore, we optimize the EU’s outage probability under the CU’s outage constraint and total transmit power limits by adjusting the power allocation coefficient for the CU and the transmit powers for both the BS and CU. To simplify the optimization process, we reduce the number of variables and apply the alternating optimization (AO) algorithm to break down the problem into two sub-problems. Each sub-problem is solved using the bisection search method and gradient descent algorithm (GDA). Simulation results demonstrate that FAS significantly improves outage performance, especially for the EU, and that CoNOMA notably captures the potential of FAS beyond NOMA and OMA, offering a promising solution for future wireless networks.

Networked integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for next-generation wireless networks, where dedicated target monitoring terminals (TMTs) can be extensively leveraged for their low-cost flexible deployment and capability to facilitate bistatic and multistatic sensing. Nevertheless, the coordinated beamforming design for networked ISAC tailored for time-of-arrival (ToA)-based multi-TMT localization remains largely unexplored. To address this gap, we present a comprehensive study in this paper. Specifically, we first establish signal models for both communication and localization, and, for the first time, derive a closed-form Cramér-Rao lower bound (CRLB) to quantify the localization performance. Leveraging this CRLB, we formulate two optimization problems focusing on sensing-centric and communication-centric criteria, respectively, to thoroughly investigate the fundamental communication-localization trade-offs. For the sensing-centric problem, we develop a globally optimal algorithm based on semidefinite relaxation (SDR), applicable to scenarios where the number of BS antennas exceeds the total number of communication users. In parallel, for the communication-centric problem, we design a globally optimal algorithm for the single-BS case utilizing bisection search. To address the general cases of both problems, we propose a unified and efficient successive convex approximation (SCA)-based algorithm, which is further extended to multi-target scenarios. Finally, simulation results demonstrate the effectiveness of our proposed algorithms, reveal the intrinsic trade-offs between communication and localization, and further show that deploying more TMTs is more beneficial than deploying more BSs in networked ISAC systems.

Fluid antenna (FA) technology has emerged as a promising approach in wireless communications due to its capability of providing increased degrees of freedom (DoFs) and exceptional design flexibility. This paper addresses the challenge of direction-of-arrival (DOA) estimation for aligned received signals (ARS) and non-aligned received signals (NARS) by designing two specialized uniform FA structures under time-constrained mobility. For ARS scenarios, we propose a fully movable antenna configuration that maximizes the virtual array aperture, whereas for NARS scenarios, we design a structure incorporating a fixed reference antenna to reliably extract phase information from the signal covariance. To overcome the limitations of large virtual arrays and limited sample data inherent in time-varying channels (TVC), we introduce two novel DOA estimation methods: TMRLS-MUSIC for ARS, combining Toeplitz matrix reconstruction (TMR) with linear shrinkage (LS) estimation, and TMR-MUSIC for NARS, utilizing sub-covariance matrices to construct virtual array responses. Both methods employ Nyström approximation to significantly reduce computational complexity while maintaining estimation accuracy. Theoretical analyses and extensive simulation results demonstrate that the proposed methods achieve underdetermined DOA estimation using minimal FA elements, outperform conventional methods in estimation accuracy, and substantially reduce computational complexity.

A novel pinching-antenna systems (PASS)-enabled secure wireless communication framework is proposed. By dynamically adjusting the positions of dielectric particles, namely pinching antennas (PAs), along the waveguides, PASS introduces a novel concept of pinching beamforming to enhance the performance of physical layer security. A fundamental PASS-enabled secure communication system is considered with one legitimate user and one eavesdropper. Both single-waveguide and multiple-waveguide scenarios are studied. 1) For the single-waveguide scenario, the secrecy rate (SR) maximization is formulated to optimize the pinching beamforming. A PA-wise successive tuning (PAST) algorithm is proposed, which ensures constructive signal superposition at the legitimate user while inducing a destructive legitimate signal at the eavesdropper. 2) For the multiple-waveguide scenario, artificial noise (AN) is employed to further improve secrecy performance. A pair of practical transmission architectures are developed:waveguide division (WD)andwaveguide multiplexing (WM). The key difference lies in whether each waveguide carries a single type of signal or a mixture of signals with baseband beamforming. For the SR maximization problem under the WD case, a two-stage algorithm is developed, where the pinching beamforming is designed with the PAST algorithm and the baseband power allocation among AN and legitimate signals is solved using successive convex approximation (SCA). For the WM case, an alternating optimization algorithm is developed, where the baseband beamforming is optimized with SCA and the pinching beamforming is designed employing particle swarm optimization. Numerical results demonstrate that i) PASS can significantly improve the secrecy performance over conventional antenna systems in both scenarios; ii) the proposed PAST algorithm for the single-waveguide scenario is efficient, especially when the number of PAs is even or large; iii) WM provides higher and more stable performance at the cost of increased complexity, while WD serves as a simple yet scalable alternative, which is effective when a large number of PAs are deployed.

Unmanned aerial vehicle (UAV) communication is of crucial importance for heterogeneous practical wireless communications. However, it is susceptible to the severe spectrum scarcity with the rapidly expanding market of wireless broadband, multimedia users, and high data-rate applications. Exploring the underutilized unlicensed spectrum through spectrum sharing is promising to tackle this issue, but the openness of the unlicensed spectrum makes UAVs susceptible to security threats from potential jammers. Therefore, a licensed and unlicensed UAV spectrum sharing network against uncertain jamming attack is studied. Moreover, to overcome the high complexity of the pure model-based optimization resource allocation schemes, the low learning efficiency and strong data dependency of data-driven deep reinforcement learning (DRL) methods, a novel optimization-driven DRL framework is proposed for the resource allocation. In particular, a model-based optimization module is exploited to derive the worst-case lower bound and a better informed target value of the formulated complex non-convex optimization problem. Furthermore, the model-based informed target value is integrated into the DRL to guide the agents for better strategies. Simulation results demonstrate that our proposed scheme can significantly improve the convergence speed and achieve a better reward performance than the pure DRL based scheme. It is also shown that the exploitation of the unlicensed spectrum can achieve approximately twice the sum transmission rate compared to using only the licensed spectrum.

In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple communication users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations.

Artificial noise (AN) has been recognized as an effective physical-layer security scheme impairing the eavesdropper (Eve). Recently, artificial noise elimination (ANE) has emerged as a promising strategy to mitigate the impact of AN at Eves. However, conventional ANE schemes rely on prior knowledge, such as legitimate channel state information (CSI) or classification information, which may limit their practical applicability. To address these practical challenges, we propose an ANE scheme beyond prior knowledge (BPK) by leveraging machine learning algorithms. Firstly, a coarse projection is applied to partially eliminate the impact of AN using maximum likelihood estimation on the equivalent AN matrix. Secondly, a density clustering algorithm is introduced to obtain classification information based on the coarsely-projected observed vectors. Thirdly, a generalized principal component analysis (PCA)-based ANE algorithm is developed to effectively mitigate the residual AN using the obtained classification information. Furthermore, the artificial-noise-to-signal ratio (ANSR) and computational complexity are analyzed for performance revaluation, and a redefinition of several AN design principles is provided for scenarios involving a powerful Eve equipped with the BPK-ANE scheme by deriving the validity boundary. Finally, numerical results reveal key insights into four principles of AN: 1) Allocating less power to AN; 2) Reducing the randomness of AN; 3) Increasing the number of transmit antennas; and 4) Increasing the modulation order.

We propose an innovative physical layer authentication method, leveraging deep learning to robustly safeguard millimeter wave communications against pilot contamination and clone attacks. Unlike traditional upper-layer authentication mechanisms, our method capitalizes on the spatial-temporal characteristics of millimeter wave channels to extract unique fingerprints, thus establishing a lightweight channel-based authentication technique. Existing methods largely overlook pilot contamination attacks, which may severely degrade the performance of physical layer authentication. Furthermore, traditional threshold-based methods struggle to differentiate between multiple nodes, while supervised learning-based methods are practically constrained due to the unavailability of attackers’ instantaneous channel state information. Moreover, traditional real-valued deep neural networks are inefficient in utilizing the phase information of complex-valued channels, rendering them inadequate for designing practical physical layer authentication schemes. To address these challenges, we propose an autoencoder, empowered by an alternating direction method of multipliers, which can detect and mitigate pilot contamination attacks by exploiting the inherent sparsity of channels. Subsequently, we design a weighted loss function to optimize the proposed classifiable autoencoder to strike an effective balance between detecting clone attacks and authenticating multiple nodes. Finally, to further enhance feature extraction from complex-valued channels, we customize a complex-valued classifiable autoencoder incorporating an innovative complex-valued long short-term memory module. Our simulation results unveil that the proposed method significantly outperforms existing approaches in maintaining high authentication accuracy even under pilot contamination, achieving a desirable trade-off between false alarm and detection rates. Additionally, our proposed complex-valued neural networks further enhance the accuracy of clone attack detection and multiple legitimate nodes authentication.

Integrated sensing and communication (ISAC) is expected to enable the fifth-generation (5G) networks to provide ubiquitous communication and sensing. However, some high-dynamic scenarios hinder applications of conventional ISAC schemes owing to the high overhead and poor real-time performance. In this paper, we design a novel ISAC architecture and propose a large language model (LLM) based two-stage beamforming prediction scheme. Specifically, in the first stage, we develop an LLM-based approach to predict the future channel state information (CSI) according to the history echoes. Via the data preprocessing and supervised fine-tuning, the LLM can achieve effective channel prediction task with unstructured data. In the second stage, according to the predicted/estimated CSI, we formulate a beamforming optimization problem to maximize the achievable sum rate while satisfying the quality of service (QoS). Then, we propose a Primary-dual network with the unsupervised adversarial learning to handle it, facilitating the on-line beamforming. Simulation results verify that, compared with the benchmarks, our proposed beamforming prediction scheme not only enjoys a higher channel prediction accuracy but also achieves a better balance between the performance and computational complexity.

Due to circuit failures, defective elements that cannot adaptively adjust the phase shifts of their impinging signals in a desired manner may exist on an intelligent reflecting surface (IRS). Traditional way to locate these defective IRS elements requires a thorough diagnosis of all the circuits belonging to a huge number of IRS elements, which is practically challenging. In this paper, we will devise novel approaches under which a transmitter sends known pilot signals and a receiver localizes all the defective IRS elements just based on its over-the-air measurements reflected from the IRS. Specifically, given any set of IRS elements, we propose an efficient method to process the received signals to determine whether this cluster contains defective elements or not with a very high accuracy probability. Based on this method, we show that the over-the-air diagnosis problem belongs to the 20 questions problem, where we can adaptively change the query set at the IRS so as to localize all the defective elements as quickly as possible. Along this line, we first propose a sorted posterior matching (sortPM) based method according to the noisy 20 questions technique, which enables accurate diagnosis even if the answers about the existence of defective elements in some sets of interest are wrong at certain question and answer (Q&A) rounds due to the noisy received signals. Next, to reduce the complexity, we propose a bisection based method according to the noiseless 20 questions technique, which totally trusts the answer at each Q&A round and keeps removing half of the remaining region based on such answers. Via numerical results, we show that our proposed methods can exploit the over-the-air measurements to localize all the defective IRS elements quickly and accurately.

Flexible intelligent metasurface (FIM) technology has emerged as a promising solution for enhancing wireless communication performance. In contrast to traditional rigid reconfigurable intelligent surfaces (RIS), an FIM consists of an array of electromagnetic (EM) elements, each capable of flexibly adjusting its position along the direction perpendicular to the surface to collaboratively morph the surface shape. In this paper, an optimization problem for maximizing the weighted sum-rate (WSR) in an FIM-aided multicell multi-user multiple-input single-output (MU-MISO) system is investigated. We jointly optimize the beamforming at the base station (BS), the phase shift matrix, and the FIM surface shape. To address this problem, we propose an efficient alternating optimization framework, where we employ the weighted minimum mean square error (WMMSE) method to reformulate the problem and the block coordinate descent (BCD) algorithm to iteratively update the variables. Specifically, we utilize the Riemannian Conjugate Gradient (RCG) algorithm to optimize the phase shift matrix, and the projected gradient descent (PGD) method to optimize the FIM surface shape. Additionally, the optimal beamforming vectors are obtained in closed form. Finally, simulation results demonstrate the superiority of FIM over conventional RIS in various scenarios.

In this paper, we propose a novel polarized six-dimensional movable antenna (P-6DMA) to enhance the performance of wireless communication cost-effectively. Specifically, the P-6DMA enables polarforming by adaptively tuning the antenna’s polarization electrically as well as controls the antenna’s rotation mechanically, thereby exploiting both polarization and spatial diversity to reconfigure wireless channels for improving communication performance. First, we model the P-6DMA channel in terms of transceiver antenna polarforming vectors and antenna rotations. We then propose a new two-timescale transmission protocol to maximize the weighted sumrate for a P-6DMA-enhanced multiuser system. Specifically, antenna rotations at the base station (BS) are first optimized based on the statistical channel state information (CSI) of all users, which varies at a much slower rate compared to their instantaneous CSI. Then, transceiver polarforming vectors are designed to cater to the instantaneous CSI under the optimized BS antennas’ rotations. Under the polarforming phase shift and amplitude constraints, a new polarforming and rotation joint design problem is efficiently addressed by a low-complexity algorithm based on penalty dual decomposition, where the polarforming coefficients are updated in parallel to reduce computational time. Simulation results demonstrate the significant performance advantages of polarforming, antenna rotation, and their joint design in comparison with various benchmarks without polarforming or antenna rotation adaptation.

The integration of network function virtualization (NFV) with mobile edge computing (MEC) fosters a more agile service provisioning in a network operational cost-efficient manner. However, some challenges exist in adapting to the unpredictable network stochastics and resource restrictiveness, when placing virtualized network functions (VNFs) or service function chains (SFSs) appropriately onto MEC networks. In this work, we study the cost-efficient online SFC deployment in MEC networks, where each service is translated as an SFC flow and traverses through networks to meet service demands. First, we formulate a long-term time-averaged network operational cost minimization problem, by optimizing both SFC mapping and flow routing, to keep the system stability. Then, to deal with the non-trivial mixed-integer programming (MIP) and stochasticity properties in the SFC deployment, we use both Lp(0 <p< 1) norm-based relaxation and penalization, and learn-and-adapt techniques, to obtain an improved performance-stability tradeoff. Finally, both theoretical analyses and numerical simulations are conducted to demonstrate the proposed method’s superiority, in terms of its asymptotic optimality and reduced queue backlog.

Aiming to circumvent the low energy efficiency (EE) dilemma of multiple-input multiple-output (MIMO) systems induced by employing hundreds of antennas, this paper investigates the potentials of stacked metasurface (SM) and semantic communications (SemCom) for achieving energy-efficient holographic communications in MIMO systems. Specifically, SM enables hybrid beamforming with increased degrees of freedom (DoFs) and reduced energy consumption, while SemCom transmits dramatically compressed key informantion that comes with low power consumption and high EE. To this end, we formulate a worstcase semantic EE (Sem-EE) maximization problem in terms of the transmit beamformer and SM's phase shifts. By proposing a semantic majorization-minimization to handle the fractional and quasi-convex Sem-EE form, quadratically constrained quadratic programs and cyclic coordinate descent can be exploited to solve the optimization variables with low computational complexity. Numerical simulations demonstrate the enhanced EE performance of SMaided semantic beamforming scheme compared to the conventional MIMO systems.

Integrated sensing and communication (ISAC) can boost the spectrum efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, it may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning aided transceiver design for ISAC. Particularly, the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio is minimized subject to the constraints of constant modulus signal and waveform similarity by transceiver design. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to solve this non-convex optimization problem. To reduce the complexity, we propose a deep unfolding neural network (NN), which can unfold the underlying ADMMbased iterative algorithm to a lightweight NN with some learnable parameters and circumvent the bisection method using the projected gradient descent. Simulation results demonstrate the effectiveness of our proposed deep unfolding NN.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key architectural element in 6G wireless systems, enabling programmable control over electromagnetic (EM) propagation. However, prevailing RIS models are limited in two critical ways: (i) they assume deterministic or Gaussian signal environments, neglecting the inherent spatial randomness of RIS deployments and user locations; and (ii) they treat EM signals as scalars, overlooking the intrinsic multigrade (e.g., scalar, vector, bivector) structure of physical fields. In this paper, we introduce CRISP, a novel Clifford Wiener–Itô Poisson chaos framework for RIS modeling under spatial randomness. By combining stochastic geometry (via Poisson point process), multivector Clifford algebra, and Wiener–Itô chaos expansions, we develop a grade-separated stochastic representation of RIS channels. This formulation enables optimal RIS beamforming via per-grade chaos projection, yielding closed-form expressions for grade-wise residual power. Extensive simulations show that CRISP achieves 10–15 dB lower residual power compared to scalar RIS baselines under Poisson-deployed scattering. Our framework provides a new mathematical foundation for RIS optimization in random and semantic wireless environments, and opens new directions in multigrade-aware, chaos-driven RIS control for 6G and beyond.

Abstract Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

This letter rethinks the probabilistic broadcast gossip scheme to achieve fast distributed average consensus in wireless networks. The consensus attainment in this scheme is heavily influenced by the broadcast probability of each node, which directly affects the convergence rate. To reduce communication costs for achieving consensus, we formulate an optimization problem to determine the optimal broadcast probability for each node. This problem involves a challenging nonconvex spectral radius term in the objective function. To address this challenge, we introduce an enhanced majorization-minimization-based approach that leverages a novel surrogate function to effectively upper bound the spectral radius function. Simulation results show that the proposed method provides substantial performance improvements over existing heuristic methods for broadcast probability optimization.

In this article, we exploit the positioning results for communication, and propose an integrated positioning and communication (IPAC) framework for terahertz (THz) massive multi-input-multioutput (MIMO) networks. Specifically, we derive an explicit expression for the Cramér-Rao bound (CRB), which is used to evaluate the positioning performance. Furthermore, based on the established relationship between positioning and communication, we propose a joint beam tracking and power allocation scheme for mobile users in THz massive MIMO networks, which minimizes the positioning error under both the data transmission outage constraint and total power constraints. Unfortunately, the joint beam tracking and power allocation optimization problem is nonconvex, and intractable due to the outage constraint. To address this challenge, we decompose the nonconvex problem into a beam tracking subproblem and a power allocation subproblem, and propose a proximal policy optimization beam tracking (PPO-BT) algorithm for the beam tracking subproblem and a robust power allocation (RPA) algorithm for the power allocation subproblem. Furthermore, we extend the proposed THz IPAC scheme to more practical 3-D scenarios. Simulation results demonstrate that our proposed THz IPAC framework can satisfy positioning and communication requirements at the same time, and our proposed methods outperform existing methods.

In this article, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multiuser broadcast (broadcast communication (BC)) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multiuser semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multiuser interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multiuser interference. More importantly, SFDMA can protect the privacy of users’ semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta–Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme.

To achieve stringent performance requirements in next generation wireless networks, such as ultra-high data rates, ubiquitous connectivity, and extremely high reliability, this paper proposes a radically novel rate splitting assisted dual-polarized stacked metasurface (RS-DPSM) transceiver architecture. In this architecture, a multi-layer dual-polarized metasurface is stacked at the active antennas and its two inherent polarizations are implemented to enable RS’s common and private messages in parallel. In sharp contrast to the conventional multiple-input multiple-output (MIMO) and metasurface-based transceiver designs, our proposed transceiver is capable of enhancing the channel capacity and introducing multi-dimensional degrees of freedom (DoFs) in the power, spatial, and polarization domains, thus enabling multi-functional, broad-spectrum, and all-time/domain/space communications without requiring massive radio-frequency (RF) chains. In addition, we derive new analytical expressions for the upper bounds of RS-DPSM transceiver’s channel capacity and ergodic sum rate, and provide some key insights. To highlight its potential benefits, we apply the proposed RS-DPSM transceiver to anti-jamming communications, and formulate a generalized sum rate maximization problem under the jammer’s imperfect angular channel state information and unknown cross-polarization discrimination. To enable an efficient resource management under the above practical conditions, we present a low-complexity optimization framework by leveraging the discretization method, properties of the quadratic function, reduced-majorization-minimization algorithm, and block successive upper-bound minimization, which admit the semi-closed-form solutions. Finally, our numerical simulations verify the superiority of our proposed transceiver architecture and optimization framework over key benchmarks.

Affine frequency division multiplexing (AFDM) is a novel modulation technique based on chirp signals that has been recently proposed as an effective solution for highly reliable communications in high-mobility scenarios. In this paper, we focus on the design of robust index modulation (IM) schemes under the multiple-antenna AFDM transmission framework. To this end, the cyclic delay diversity (CDD) technique is employed to harvest the transmit diversity gain. As a result, we propose two novel AFDM-IM schemes with transmit diversity, termed as CDD-AFDM-IM-I and CDD-AFDM-IM-II. We analyze the full diversity conditions and parameter settings of the proposed CDD-AFDM-IM schemes for both integer and fractional Doppler cases over linear time-varying (LTV) channels. Moreover, we prove that IM enables AFDM to have stronger diversity protection when the full diversity condition is not satisfied. Asymptotically tight upper bounds on the average bit error rates (BERs) of the proposed schemes with maximum-likelihood (ML) detection are derived in closed-form. Furthermore, we propose a low-complexity double-layer message passing (DLMP) algorithm for practical large-dimensional signal detection in the proposed CDD-AFDM-IM systems. Comparison with existing detections shows that the proposed DLMP algorithm achieves a better tradeoff between the BER performance and the computational complexity. Finally, BER simulation results confirm that our proposed CDD-AFDM-IM schemes with both the ML and DLMP detections outperform the benchmark schemes over the LTV channels.

Frequency diverse array (FDA) is a promising antenna technology to achieve physical layer security by varying the frequency of each antenna at the transmitter. However, when the channels of the legitimate user and eavesdropper are highly correlated, FDA is limited by the frequency constraint and cannot provide satisfactory security performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where the positions of antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the secrecy capacity by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on block successive upper-bound minimization (BSUM) method. Moreover, to evaluate the security performance provided by MFDA, we introduce two benchmark schemes, i.e., phased array (PA) and FDA. Simulation results demonstrate that MFDA can significantly enhance security performance compared to PA and FDA. In particular, when the frequency constraint is strict, MFDA can further increase the secrecy capacity by adjusting the positions of antennas instead of the frequencies.

The frequency diverse array (FDA) is highly promising for improving covert communication performance by adjusting the frequency of each antenna at the transmitter. However, when faced with the cases of multiple wardens and highly correlated channels, FDA is limited by the frequency constraint and cannot provide satisfactory covert performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where positions of the antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the covert rate by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on the block successive upper-bound minimization (BSUM) method. Moreover, considering the challenge of obtaining perfect channel state information (CSI) at multiple wardens, we study the case of imperfect CSI. Simulation results demonstrate that MFDA can significantly enhance covert performance compared to the conventional FDA. In particular, when the frequency constraint is strict, MFDA can further increase the covert rate by adjusting the positions of antennas instead of the frequencies.

Spectrum sharing unmanned aerial vehicle (UAV) network is a promising technology for future communication systems to mitigate the spectrum scarcity problem. However, the future sixth-generation large-scale wireless communication networks are expected not only to provide a high data rate for massive numbers of users but also to meet their stringent service requirements. Particularly in dynamic spectrum sharing UAV networks, the coupling of multi-dimensional resources and diverse user demands make the efficient and real-time resource allocation exceptionally challenging. A partially observable deep multi-agent active inference (PODMAI) framework is proposed to tackle these issues. The variational free energy is minimized to update the policy exploiting the belief based learning method. A decentralized training and execution multi-agent strategy is designed to navigate the challenges posed by partially observable information. To further satisfy the dynamic user demand and supplement partial observations, a joint spatial-temporal-attention prediction network is designed to construct the demand prediction enhanced PODMAI framework for resource allocation. Exploiting the established framework, an intelligent spectrum allocation and trajectory optimization scheme is elaborated for a spectrum sharing UAV network with multi-modal dynamic transmission rate demands. Simulation results demonstrate that our proposed scheme outperforms benchmark schemes in terms of the network sum transmission rate. Additionally, our proposed scheme exhibits faster convergence compared to the conventional reinforcement learning. Overall, our proposed framework can enrich intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

Integrated sensing and communication (ISAC) has evolved as a critical paradigm to enhance the dual functions concurrently. However, ISAC may encounter performance limitations, due to undesired channel conditions, small target size, and security threats. In this paper, we investigate intelligent reconfigurable surface (IRS)-aided bi-static ISAC networks, where the IRS is mounted directly on the target surface, and analyze the signal enhancing and suppressing effects of the target-mounted IRS, respectively. First, we maximize the sensing signal-to-noise ratio (SNR) while satisfying the users’ communication requirements by jointly optimizing the transmit beamforming and IRS reflection. To solve this optimization problem, an alternating optimization algorithm is employed to decouple the optimization variables, followed by the application of successive convex approximation and penalty dual decomposition to solve the subproblems. Second, we consider two threatening scenarios where two adversarial base stations (BSs) intend to capture the information reflected by the target. In the first scenario where the adversarial receiving BS attempts to exploit the reflected ISAC signal, we minimize its received power via optimizing the transmit beamforming and the IRS reflection alternately. In the second scenario where the adversarial transmitting BS emits a dedicated signal to detect the target, we focus on optimizing the IRS reflection. Simulation results are presented to show the effectiveness of the proposed schemes.

It is challenging to ensure both high accuracy and low complexity when localizing radiation sources. To address this challenge, we propose two novel methods leveraging time difference of arrival (TDOA) measurements. Specifically, we introduce a TDOA estimation method and a direct position determination (DPD) method based on parallel factor (PARAFAC) analysis in the cross-correlation domain. Initially, multiple sensors synchronously capture the source signal, and the cross-correlation function between signals received from a reference sensor and other sensors is calculated. Then, the primary cross-spectrum data undergoes an expansion and integration process to establish the PARAFAC model. Through cross-spectrum expansion, virtual nodes are formed, which further improves the estimation performance. The TDOA estimates for each sensor are obtained by normalizing and extracting the phase from this matrix. Additionally, we introduce a novel DPD method tailored for multipath propagation scenarios. Simulations and real-world measurements demonstrate the superiority and effectiveness of our proposed methods compared with cutting-edge methods.

Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, we propose an Alpha-Beta-Gamma (ABG) formula to model the relationship between the end-to-end measurement and SNR, which can be applied for both image reconstruction tasks and inference tasks. Specifically, for image reconstruction tasks, the proposed ABG formula can well fit the commonly used DL networks, such as SCUNet, and Vision Transformer, for semantic encoding with the multi scale-structural similarity index measure (MS-SSIM) measurement. Furthermore, we find that the upper bound of the MS-SSIM depends on the number of quantized output bits of semantic encoders, and we also propose a closed-form expression to fit the relationship between the MS-SSIM and quantized output bits. To the best of our knowledge, this is the first theoretical expression between end-to-end performance metrics and SNR for semantic communications. Based on the proposed ABG formula, we investigate an adaptive power control scheme for semantic communications over random fading channels, which can effectively guarantee quality of service (QoS) for semantic communications, and then design the optimal power allocation scheme to maximize the energy efficiency of the semantic communication system. Furthermore, by exploiting the bisection algorithm, we develop the power allocation scheme to maximize the minimum QoS of multiple users for OFDMA downlink semantic communication Extensive simulations verify the effectiveness and superiority of the proposed ABG formula and power allocation schemes.

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to severe challenges, namely, their limited computation, energy and communication resources, which limits the achievable detection performance. To overcome these challenges, a UAV cognitive semantic communication system is proposed by exploiting a knowledge graph. Moreover, we design a multi-scale codec for semantic compression to reduce data transmission volume while guaranteeing detection performance. Considering the complexity and dynamicity of UAV communication scenarios, a signal-to-noise ratio (SNR) adaptive module with robust channel adaptation capability is introduced. Furthermore, an object detection scheme is proposed by exploiting the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that our proposed semantic communication system outperforms benchmark systems in terms of detection accuracy, communication robustness, and computation efficiency, especially in dealing with low bandwidth compression ratios and low SNR regimes.

This paper investigates a highly spectrally efficient transmission scheme in an integrated satellite and aerial network (ISAN). Specifically, we first propose a novel uplink access framework, where the co-design of communication and over-the-air computation (AirComp) is implemented through layer division multiplexing (LDM) in the aerial network, while the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) technology is employed in the satellite network. Then, according to the proposed framework, we mathematically formulate a joint optimization problem that aims at maximizing the system achievable sum rate, subject to the constraints of minimal accuracy requirement of AirComp and minimal quality-of-service requirements of communication service. Next, by introducing the inter-network interference-related auxiliary variable, we divide the original optimization problem into two subproblems associated with the optimization of the satellite and aerial networks. To tackle the first subproblem, we propose a beamspace-inspired analog beamforming (BF) method, and derive closed-form expressions for BF vectors and transmit powers to implement the CR-NOMA scheme in the satellite network. Meanwhile, to address the second subproblem, we propose a beamspace-inspired digital BF together with successive convex approximation and alternating optimization approaches, to obtain the BF matrices, transmit power coefficients and AirComp scaling factor, so that the LDM-based communication and computation co-design (CCCD) can be realized in the aerial network. Moreover, for complexity reduction, we propose a beamspace-inspired zero-forcing BF method to calculate the communication BF matrices, and then leverage the orthogonal beam superposition approach to obtain the computation BF matrix, thereby presenting another CCCD scheme. Finally, our simulation results confirm that since the proposed schemes can realize spectrum multiplexing for communication and AirComp services, we achieve higher system spectral efficiency and lower computation error than the benchmarks.

We propose a joint covert beamforming design and jamming strategy to protect the communication process between Alice and Bob from being discovered by Willie with the help of another pair of neutral nodes. Specifically, with the help of irrelevant communication parties that commonly exist in practical communication scenarios, Jammer increases his transmission power by interfering with the neutral receiver, thus indirectly increasing the interference to Willie, which can be viewed as leveraging the force to make a big impact with a small effect. In our designed beamformer, we jointly optimize the beam power allocation factor, Alice’s transmission power, and Jammer’s transmission power when Alice transmits, to maximize the covert rate, which also maximizes Alice’s transmission power. In the perfect channel state information (CSI) scenario, the transformed optimization problem is solved via a one-dimensional search method and CVX solver. Due to the solution’s high complexity, we further propose a method to determine the optimal power allocation factor. For the imperfect Willie’s CSI scenario, three cases are investigated: Alice to Willie imperfect CSI, Transmitter to Willie imperfect CSI, and Jammer to Willie imperfect CSI. We utilize the S-procedure to tackle the optimization problem. Simulation results demonstrate the effectiveness of our proposed strategy.

In this paper, we consider the downlink transmission of a multi-antenna base station (BS) supported by an active simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS) to serve single-antenna users via simultaneous wireless information and power transfer (SWIPT). In this context, we formulate an energy efficiency maximisation problem that jointly optimises the gain, element selection and phase shift matrices of the active STAR-RIS, the transmit beamforming of the BS and the power splitting ratio of the users. With respect to the highly coupled and non-convex form of this problem, an alternating optimisation solution approach is proposed, using tools from convex optimisation and reinforcement learning. Specifically, semi-definite relaxation (SDR), difference of convex functions (DC), and fractional programming techniques are employed to transform the non-convex optimisation problem into a convex form for optimising the BS beamforming vector and the power splitting ratio of the SWIPT. Then, by integrating meta-learning with the modified deep deterministic policy gradient (DDPG) and soft actor-critical (SAC) methods, a combinatorial reinforcement learning network is developed to optimise the element selection, gain and phase shift matrices of the active STAR-RIS. Our simulations show the effectiveness of the proposed resource allocation scheme. Furthermore, our proposed active STAR-RIS-based SWIPT system outperforms its passive counterpart by 57% on average.

In this paper, we propose a novel multi-functional reconfigurable intelligent surface (MF-RIS) that supports signal reflection, refraction, amplification, and target sensing simultaneously. Our MF-RIS aims to enhance integrated communication and sensing (ISAC) systems, particularly in multi-user and multi-target scenarios. Equipped with reflection and refraction components (i.e., amplifiers and phase shifters), MF-RIS is able to adjust the amplitude and phase shift of both communication and sensing signals on demand. Additionally, with the assistance of sensing elements, MF-RIS is capable of capturing the echo signals from multiple targets, thereby mitigating the signal attenuation typically associated with multi-hop links. We propose a MF-RIS-enabled multi-user and multi-target ISAC system, and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of sensing targets. This problem involves jointly optimizing the transmit beamforming and MF-RIS configurations, subject to constraints on the communication rate, total power budget, and MF-RIS coefficients. We decompose the formulated non-convex problem into three sub-problems, and then solve them via an efficient iterative algorithm. Simulation results demonstrate that: 1) The performance of MF-RIS varies under different operating protocols, and energy splitting (ES) exhibits the best performance in the considered MF-RIS-enabled multi-user multi-target ISAC system; 2) Under the same total power budget, the proposed MF-RIS with ES protocol attains$\rm {52.2}\%$,$\rm {73.5}\%$, and$\rm {60.86}\%$sensing SINR gains over active RIS, passive RIS, and simultaneously transmitting and reflecting RIS (STAR-RIS), respectively; 3) The number of sensing elements will no longer improve sensing performance after exceeding a certain number.

Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth-generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven schemes have achieved high accuracy in AMC. However, most of these schemes focus on generating additional prior knowledge of unknown signals, which needs more computation cost in the inference phase. To solve these problems, we propose for the first time a modulation knowledge graph (MKG), and a novel knowledge graph (KG) driven AMC (KGAMC) framework by training the networks under the guidance of MKG domain knowledge. To achieve the best performance by exploiting KGAMC, a KG-driven multi-time-scale network (KG-MTSNet) is proposed to extract the MKG knowledge and the scale and frequency features of the sampled signals. Moreover, to utilize the knowledge, a designed feature aggregation loss is implemented to improve the signal feature presentation obtained by the data-driven model. Simulation results demonstrate that KGAMC significantly boosts the performances of data-driven models, and the KG-MTSNet achieves a superior classification performance compared to other benchmarks. Furthermore, the effectiveness of KGAMC is demonstrated in terms of the interpretability of the feature extraction and the sample shortage situation.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading.

To achieve sustainable communication and sensing, simultaneous wireless information and power transfer (SWIPT) has been introduced into integrated sensing and communication (ISAC). However, this combination brings significant security challenges due to signal multiplexing and spectrum sharing. In this paper, an active intelligent reflecting surface (IRS) assisted secure integrated sensing and SWIPT system is proposed with the power splitting (PS) model adopted. To maximize the harvested power while satisfying the constraints of sidelobe level ratio and secrecy rate, a problem is formulated to jointly optimize the transmit beamforming, artificial noise (AN) vectors, PS ratios, and amplification factors and phase shifts of active IRS, which is difficult to solve due to the coupled variables. To this end, we decompose it into two sub-problems, and propose two alternating optimization (AO) algorithms to solve them. First, an AO algorithm based on semi-definite relaxation (SDR) is developed. Specifically, we develop a two-layer algorithm to obtain the transmit beamforming matrix, AN covariance matrix and PS ratios, and utilize the penalty-based method to design the coefficients of active IRS. To reduce the complexity caused by the high-dimensional matrix operation of SDR, an AO algorithm based on successive convex approximation (SCA) is proposed, which can approximate the original problem as a sequence of convex counterparts via the first-order Taylor expansion. Simulation results show that the SCA-based AO algorithm can achieve the performance close to that of SDR with lower complexity.

Integrated sensing and communication (ISAC) can enhance spectral efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, effective operation of ISAC may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning-aided transceiver design scheme for ISAC in a cluttered environment. In particular, we optimize the transmit waveform and receive filtering to minimize the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio (SINR), while adhering to the constraints of a constant modulus signal and waveform similarity. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to address this non-convex optimization problem with both equality and inequality constraints. To further reduce the computational complexity, we develop two deep unfolding neural networks (NNs), termed ADMM-DL-NET and ADMM-PGD-NET, to handle this problem, which can unfold the underlying ADMM-based iterative algorithm to a lightweight neural network with learnable parameters and eliminate the need for the bisection method by adopting the Uzawa’s method and projected gradient descent, respectively. Simulation results demonstrate that our proposed deep unfolding NNs can achieve comparable performance to the ADMM-based iterative algorithm with significantly reduced complexity, and outperform the unsupervised learning benchmarks in performance and number of learnable parameters.

A novel time-efficient framework is proposed for improving the robustness of a broadband multiple-input multiple-output (MIMO) system against unknown interference under rapidly-varying channels. A mean-squared error (MSE) minimization problem is formulated by optimizing the beamformers employed. Since the unknown interference statistics are the premise for solving the formulated problem, an interference statistics tracking (IST) module is first designed. The IST module exploits both the time- and spatial-domain correlations of the interference-plus-noise (IPN) covariance for the future predictions with data training. Compared to the conventional signal-free space sampling approach, the IST module can realize zero-pilot and low-latency estimation. Subsequently, an interference-resistant hybrid beamforming (IR-HBF) module is presented, which incorporates both the prior knowledge of the theoretical optimization method as well as the data-fed training. Taking advantage of the interpretable network structure, the IR-HBF module enables the simplified mapping from the interference statistics to the beamforming weights. The simulations are executed in high-mobility scenarios, where the numerical results unveil that: 1) the proposed IST module attains promising prediction accuracy compared to the conventional counterparts under different snapshot sampling errors; and 2) the proposed IR-HBF module achieves lower MSE with significantly reduced computational complexity.

Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of blind signals, which consumes longer computation time and ignores the interpretability of the model learning process. To solve these problems, we propose a novel knowledge graph (KG) driven AMC (KGAMC) scheme by training the networks under the guidance of domain knowledge. A modulation knowledge graph (MKG) with the knowledge of modulation technical characteristics and application scenarios is constructed and a relation-graph convolution network (RGCN) is designed to extract knowledge of the MKG. This knowledge is utilized to facilitate the signal features separation of the data-oriented model by implementing a specialized feature aggregation method. Simulation results demonstrate that KGAMC achieves supe-rior classification performance compared to other benchmark schemes, especially in the low signal-to-noise ratio (SNR) range. Furthermore, the signal features of the high-order modulation are more discriminative, thus reducing the confusion between similar signals.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

This work investigates a cognitive satellite-terrestrial covert communication network, where a satellite serves as the primary transmitter to communicate with a primary user (PU) covertly under the surveillance of an unauthorized warden and a terrestrial base station (BS) serves as the secondary transmitter to communicate with a secondary user. Considering imperfect channel state information (CSI) of warden, we jointly design both satellite's transmit power and BS's transmit beamforming to maximize the PU's covert rate while satisfying the terrestrial network's information rate requirement, satellite network's covertness and both satellite's and BS's power constraints. The successive convex approximation method is employed to obtain the optimal BS beamforming. In addition, the imperfect CSI is tackled by the$S$-procedure technique. Simulation results reveal that compared to other benchmark schemes the proposed optimization algorithm performs better in terms of the covert rate.

The rapid development of next-generation Internet of Things (IoT) applications, including integrated-sensing-and-communication (ISAC), smart grids, smart cities, intelligent transport networks, etc., enables at least tens of billions of bandwidth-thirsty IoT devices, which consume a deluge of data in the sixth-generation (6G) communication systems. In addition, future challenging heterogeneous services and applications, such as Industry 4.0, require the provisioning of unprecedented massive device access, heterogeneous data traffic, high spectral efficiency, and low latency. As a result, there is an urgent demand to pay more attention to IoT networks for high-reliable and low-delay massive access.

The reconfigurable intelligent surface (RIS) has attracted significant research interests recently due to its abilities of dynamic channel reconstruction, flexible deployment and reduced power consumption. However, a malicious RIS can introduce serious signal degradation and even interception risk. This article investigates destructive beamforming design from the perspective of a malicious RIS, where the RIS is active and able to amplify the reflected signals from the base station (BS) to an Internet of Things Device (IoTD). We consider two scenarios where the BS is known and unknown to the identity of malicious RIS, and the objective is to minimize the received signal-to-noise ratio (SNR) at the IoTD with the constraints of total power budget and RIS signal amplification. To solve the above nonconvex optimization problem, we first propose a low-complexity scheme by integrating several classical beamforming methods with the Taylor expansion approach to solve the original problem for the case of known malicious RIS at BS. While for the unknown malicious RIS case, we propose an alternating optimization scheme by using the successive convex approximation method to obtain the beamforming vector and reflection coefficient matrix iteratively. Finally, numerical results verify that, through the proposed destructive beamforming design, the RIS only brings pain without gain for the signal reception.

This paper investigates a dual-unmanned aerial vehicle (UAV) assisted short packet covert communication system in the presence of a warden. Specifically, one flying UAV serves as the base station to transmit covert information to a legitimate ground user, and the other flying UAV is deployed as a cooperative jammer to transmit artificial noise against detection by a warden. Considering a more practical scenario, where only imperfect location information of the warden is known at both UAVs, we jointly optimize both UAVs’ transmit powers and three-dimensional (3D) trajectories to maximize the average covert transmission rate under the constraints of both UAVs’ mobility, transmit powers and warden’s detection error probability (DEP). On the one hand, the incomplete Gamma function involved in warden’s DEP makes the covertness constraint intractable for further analysis. To facilitate the design, warden’s DEP is lower bounded by Pinsker’s inequality. On the other hand, the formulated optimization problem is intractable to solve directly owing to the multiple highly coupled variables and the uncertainty of the warden’s location. The alternating optimization algorithm combined with the successive convex approximation and S-procedure techniques is leveraged to solve three optimization subproblems iteratively. Numerical results reveal that superior performance can be achieved by employing the proposed joint optimization of both UAVs’ transmit powers and 3D trajectories algorithm compared with the traditional two-dimensional trajectory optimization algorithm and the scheme without the assistance of the cooperative UAV.

Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

This paper proposes an active aerial reconfigurable intelligent surface (ARIS) assisted secure communication framework by integrating sensing and positioning against a mobile eavesdropper. In the proposed scheme, the base station (BS) beamforms the private information to the legitimate user and jams the eavesdropper with artificial noise (AN), while reconfiguring the phases and amplitudes of the passive signal by the active ARIS for promoting secure communications. To acquire the channel state information of the time-vary wiretap channel, the BS tracks the position of the eavesdropper by exploiting the reflected AN. Based on the tracked position of the eavesdropper in the previous time slot, we propose a secure communication scheme that aims to maximize the secrecy rate in the current time slot. This scheme is assisted by the ARIS through jointly optimizing the passive beamforming of the privacy information and AN, the reflection matrix of the ARIS, and the position of the ARIS. In the case of this non-convex quandary with highly coupled variables, we opt to disassemble it into three constituent subproblems and design an alternating optimization framework, where the optimal power beamforming at the BS is derived using a successive convex approximation method and semi-positive definite relaxation technique, the reconfigurable coefficient of the ARIS is optimized using the majorization-minimization algorithm, and the optimal position of the ARIS using the three-dimensional network is obtained by the deep deterministic policy gradient algorithm. Simulation results demonstrate the superior performance of the proposed scheme in the context of the secrecy rate when compared with benchmark schemes. By adopting the active beamforming and positioning technique, the secrecy rate can be increased by 38.3% and 10.8%, respectively.

The ultimate goal of next generation multiple access (NGMA) is to support massive terminals and facilitate multiple functionalities over the limited radio resources of wireless networks in the most efficient manner possible. However, the random and uncontrollable wireless radio environment is a major obstacle to realizing this NGMA vision. Given the prominent feature of achieving a 360° smart radio environment, simultaneously transmitting and reflecting surfaces (STARS) are emerging as one key enabling technology among the family of reconfigurable intelligent surfaces for NGMA. This article provides a comprehensive overview of the recent research progress of STARS, focusing on fundamentals, performance analysis, and full-space beamforming design, as well as promising employments of STARS in NGMA. In particular, we first introduce the basics of STARS by elaborating on the foundational principles and operating protocols as well as discussing different STARS categories and prototypes. Moreover, we systematically survey the existing performance analysis and beamforming design for STARS-aided wireless communications in terms of diverse objectives and different mathematical approaches. Given the superiority of STARS, we further discuss advanced STARS applications as well as the attractive interplay between STARS and other emerging techniques to motivate future works for realizing efficient NGMA.

Movable antenna (MA) array is a novel technology recently developed where positions of transmit/receive antennas can be flexibly adjusted in the specified region to reconfigure the wireless channel and achieve a higher capacity. In this letter, we, for the first time, investigate the MA array-assisted physical-layer security where the confidential information is transmitted from a MA array-enabled Alice to a single-antenna Bob, in the presence of multiple single-antenna and colluding eavesdroppers. We aim to maximize the achievable secrecy rate by jointly designing the transmit beamforming and positions of all antennas at Alice subject to the transmit power budget and specified regions for positions of all transmit antennas. The resulting problem is highly non-convex, for which the projected gradient ascent (PGA) and the alternating optimization methods are utilized to obtain a high-quality suboptimal solution. Simulation results demonstrate that since the additional spatial degree of freedom (DoF) can be fully exploited, the MA array significantly enhances the secrecy rate compared to the conventional fixed-position antenna (FPA) array.

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-polarized MIMO scenarios, channels in the vertical and horizontal polarization directions tend to exhibit high polarization correlation. To fully exploit the inherent propagation similarity within dual-polarized channels, we propose a disentangled representation neural network (NN) for CSI feedback, referred to as DiReNet. The proposed DiReNet disentangles dual-polarized CSI into three components: polarization-shared information, vertical polarization-specific information, and horizontal polarization-specific information. This disentanglement of dual-polarized CSI enables the minimization of information redundancy caused by the polarization correlation and improves the performance of CSI compression and recovery. Additionally, flexible quantization and network extension schemes are designed. Consequently, our method provides a pragmatic solution for CSI feedback to harness the physical MIMO polarization as a priori information. Our experimental results show that the performance of our proposed DiReNet surpasses that of existing DL-based networks, while also effectively reducing the number of network parameters by nearly one third.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify properties of the received signal. Although many deep learning-based WSR models have been developed, they still rely on a large amount of labeled training data. Thus, they cannot tackle the few-sample problem in the practically and dynamically changing wireless communication environment. To overcome this challenge, a novel SSwsrNet framework is proposed by using the deep residual shrinkage network (DRSN) and semi-supervised learning. The DRSN can learn discriminative features from noisy signals. Moreover, a modular semi-supervised learning method that combines labeled and unlabeled data using MixMatch is exploited to further improve the classification performance under few-sample conditions. Extensive simulation results on automatic modulation classification (AMC) and wireless technology classification (WTC) demonstrate that our proposed WSR scheme can achieve better performance than the benchmark schemes in terms of classification accuracy. This novel method enables more robust and adaptive signal recognition for next-generation wireless networks.

Multi-access edge computing (MEC) has been recognized as a promising technology for enhancing the computation capability for next generation wireless networks. This paper studies physical layer security for an MEC network, where multiple users desire to securely offload part of their computation tasks to a base station (BS) simultaneously using non-orthogonal multiple access (NOMA) subject to the potential overhearing of a malicious eavesdropper. The secrecy outage probability (SOP) is adopted as a secrecy performance metric of the computation offloading against eavesdropping attacks. We aim to minimize the total energy consumption of the MEC system subject to an individual SOP constraint for each user. To this end, we jointly design each user’s local computing bits, the transmit power, the secrecy code rates, as well as the successive interference cancellation decoding order at the BS side. As the formulated problem is highly non-convex and challenging to solve, we propose an efficient algorithm based on penalty dual decomposition (PDD) and sequential convex approximation methods to obtain an efficient suboptimal solution. To reduce the computational complexity, we further propose a reverse recursion (RR) algorithm and derive semi-closed-form solutions to the design problem. Numerical results are presented to validate the convergence and the effectiveness of our proposed algorithms. We show that the minimal total energy consumption obtained via either the PDD or RR method approaches the optimal performance of exhaustive search as the task duration increases. It is also demonstrated that the RR algorithm can achieve a comparable performance to that of the PDD algorithm while enjoying a much lower computational complexity.

Semantic communication (SemCom) is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods of existing SemCom frameworks are inexplicable and inflexible, which limits the achievable performance. In this paper, to tackle this issue, a knowledge graph (KG) is exploited to develop SemCom systems. Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. Moreover, a simple, general, and interpretable semantic alignment algorithm for semantic information detection is proposed. Furthermore, an effective semantic correction algorithm is proposed by mining the inference rule from the KG. Additionally, the pre-trained model is fine-tuned to recover semantic information. For the multi-user cognitive SemCom system, a message recovery algorithm is proposed to distinguish the messages of different users by matching the knowledge level and the context at the destination. Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive SemCom systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability. Finally, we present realistic single-user and multi-user cognitive SemCom systems results by building a software-defined radio prototype system.

Wireless information surveillance has received widespread attention due to the urgency of monitoring growing suspicious communications. This paper considers a challenging surveillance scenario, where the monitor (E) intends to eavesdrop the suspicious multihop communications from a long distance to ensure concealment, leading to the eavesdropping condition undesirable. To tackle this challenging, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted surveillance strategy, where the STAR-RIS, acts as a “bridge”, is deliberately deployed between the suspicious system and E, to adaptively transmit and reflect the suspicious signal and E's jamming signal, and then facilitate E's eavesdropping. Specifically, we consider the adaptive rate transmission and the delay-limited transmission for the suspicious system, and accordingly maximize E's instantaneous and average eavesdropping rate, by jointly optimizing the passive transmission- and reflection-coefficient matrices at the STAR-RIS, the jamming set and jamming power allocations of E (across all hops). The optimization problems in both transmission modes include numerous integer and continuous variables and thus are highly non-convex. Nevertheless, we show by detailed analysis that the original problem in each mode can be solved by only considering two possible cases, where E and the STAR-RIS intend to enhance and reduce the suspicious transmission rate, respectively. More importantly, in each case, many of necessary prerequisites for achieving the optimal solution are first determined analytically. Armed with these, the optimization problem then can be solved by leveraging the successive convex approximation technique and the simple search. As demonstrated by simulation results, since our proposed strategy is adaptive in term of varying the suspicious transmission rate, it will achieve significant eavesdropping performance gain as compared to other competitive benchmarks.

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice. Such problem is well-solved via another projected gradient descent algorithm developed with a lower complexity. As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas.

Heterogeneous wireless networks (HetNets), where networks are deployed with ultra-dense small cells (SCs), is one of the main enabling technologies for future wireless networks. In such networks, signals are vulnerable to severe blockage, interference, and intermittent connectivity. This can be largely overcome using the emerging Reconfigurable Intelligent Surface (RIS) technology that can enhance HetNets performance by controlling the propagation environment. However, jointly optimizing the parameters of base stations’ (BSs’) active beamforming and RISs’ passive beamforming is a major challenge in RIS-empowered HetNets. In this paper, we investigate the issue of rate control in RIS-empowered multi-cell multiple-input single-output (MISO) HetNets via joint users’ equipment (UEs) rate fairness and SCs rate load balancing. We assume RIS-assisted SC BSs at mmWave underlying a RIS-assisted macrocell (MC) BS at sub-6GHz serving dual-connectivity UEs that can concurrently connect to the MC BS and a single SC BS. Then, we formulate an optimization problem whose objective is to jointly optimize the active transmit beamforming vectors of the MC and SCs BSs on the one hand and the passive beamforming vectors of the MC and SCs RISs on the other hand. Due to the high non-convexity and complexity of the formulated problem, we propose a novel distributed Deep Deterministic Policy Gradient (DDPG)-based multi-task deep reinforcement learning (MTDRL) scheme to solve the problem and learn network dynamics. Through deliberate definitions of MTDRL agent’s tasks and their corresponding main elements, we demonstrate via simulations that our proposed scheme guarantees a fair distribution of rates within UEs and SCs. In addition, we quantify the robustness of our proposed MTDRL scheme compared with some benchmarks in terms of convergence speed and utility values.

Aiming to circumvent the severe large-scale fading and the energy scarcity dilemma in high-altitude platform (HAP) networks, this paper investigates the benefits of the reconfigurable intelligent surface (RIS) and simultaneous wireless information and power transfer (SWIPT) on HAP communications. Specifically, we propose a concept of multi-layer refracting RIS-assisted receiver to achieve concurrent transmission of the information and energy, which is conducive to overcoming the severe fading effect induced by extreme long-distance HAP links and fully exploits RIS’s degrees-of-freedom (DoFs) for the SWIPT design. Based on the RIS-enhanced receiver, we then formulate a worst-case sum-rate maximization problem by considering the channel state information (CSI) error, the information rate requirements, and the energy harvesting constraint. To handle the intractable non-convex problem, a scalable robust optimization framework is proposed to obtain semi-closed-form solutions. Specifically, a discretization method is adopted to convert the imperfect CSI into a robust one. Then, by utilizing the LogSumExp inequality to smooth the objective and constraints, we develop a dual method to obtain the optimal solution for the HAP transmit precoder. In addition, a modified cyclic coordinate descent (M-CCD) is adopted to update the block-wise RIS coefficients. Moreover, closed-form solutions for power splitting (PS) ratios and the receive decoder are derived. Finally, the asymptotic performance of our proposed RIS-enhanced receiver is provided to reveal the substantial capacity gain for HAP communications. Numerical simulations demonstrate that the proposed architecture and optimization framework are capable of achieving superior performance with low complexity compared to state-of-the-art schemes in HAP networks.

This paper considers intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a multi-user multiple-input single-output (MISO) interference channel (IFC), where multiple transmitters (Txs) serve their corresponding receivers (Rxs) in a shared spectrum with the aid of IRSs. Our goal is to maximize the sum rate of the Rxs by jointly optimizing the transmit covariance matrices at the Txs, the phase shifts at the IRSs, and the resource allocation subject to the individual energy harvesting (EH) constraints at the Rxs. Towards this goal and based on the well-known power splitting (PS) and time switching (TS) receiver structures, we consider three practical transmission schemes, namely the IRS-aided hybrid TS-PS scheme, the IRS-aided time-division multiple access (TDMA) scheme, and the IRS-aided TDMA-D scheme. The latter two schemes differ in whether the Txs employ deterministic energy signals known to all the Rxs. Despite the non-convexity of the three optimization problems corresponding to the three transmission schemes, we develop computationally efficient algorithms to address them suboptimally, respectively, by capitalizing on the techniques of alternating optimization (AO) and successive convex approximation (SCA). Moreover, we conceive feasibility checking methods for these problems, based on which the initial points for the proposed algorithms are constructed. Simulation results demonstrate that our proposed IRS-aided schemes significantly outperform their counterparts without IRSs in terms of sum rate and maximum EH requirements that can be satisfied under various setups. In addition, the IRS-aided hybrid TS-PS scheme generally achieves the best sum rate performance among the three proposed IRS-aided schemes, and if not, increasing the number of IRS elements can always accomplish it.

This paper investigates a mobile edge computing (MEC) network assisted by an unmanned aerial vehicle (UAV), where the moving UAV and a fixed ground base station cooperatively provide MEC services for multiple ground users. To evaluate the quality of service under this architecture, we first derive the successful edge computing probability (SECP) to evaluate the service reliability. Given a target SECP requirement, we formulate a service coverage maximization problem by optimizing the UAV altitude and task offloading probability. The problem is hard to solve due to the coupled UAV altitude and task offloading probability in the derived SECP expression. To address this challenge, we first explore some interesting properties of the formulated problem, and then use these properties to develop a golden-section search based method to solve the formulated optimization problem. Numerical results are used to verify the theoretical analysis of our system and demonstrate the efficiency of the proposed scheme.

This paper investigates a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. Three efficient operating protocols for the S-IOS, namely time switching, power splitting, and mode switching, are proposed to enable the dual-functionality of energy harvesting and information transmission. For each protocol, we design a joint optimization framework of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the network sum rate. Despite the challenging non-convex optimization problems with highly coupled and/or integer optimization variables, we develop computationally-efficient algorithms to solve them in an iterative manner, which exploit the intrinsic structure of the problems and employ the penalty-based method and the successive convex approximation. Numerical results confirm the efficiency of our developed optimization algorithms, demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications, and quantify the performance advantage of the proposed designs over the baseline schemes.

Light fidelity (LiFi) is a potential key technology for future 6G networks. However, its feasibility of supporting mobile communications has not been fundamentally discussed. In this paper, we investigate the time-varying channel characteristics of mobile LiFi based on measured mobile phone rotation and movement data. Specifically, we define LiFi channel coherence time to evaluate the correlation of the channel timing sequence. Then, we derive the expression of LiFi transmission rate based on the m-pulse-amplitude-modulation (M-PAM). The derived rate expression indicates that mobile LiFi communications is feasible by using at least two photodiodes (PDs) with different orientations. Further, we propose two channel estimation schemes, and propose a LiFi channel tracking scheme to improve the communication performance. Finally, our experimental results show that the channel coherence time is on the order of tens of milliseconds, which indicates a relatively stable channel. In addition, based on the measured data, better communication performance can be realized in the multiple-input multiple-output (MIMO) scenario with a rate of 36Mbit/s, compared to other scenarios. The results also show that the proposed channel estimation and tracking schemes are effective in designing mobile LiFi systems.

With the ever-increasing user density and quality of service (QoS) demand, 5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA.

Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users’ intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.

Utilizing unmanned aerial vehicles (UAVs) as aerial platforms to provide both sensing and communication services is envisioned as a promising paradigm, due to their inherent flexibility and maneuverability. In this paper, we propose a UAV-enabled sensing-assisted communication scheme for vehicular networks using the integrated sensing and communication (ISAC) technique. Specifically, we consider the geometry of vehicles as extended targets with multiple resolvable scatters and adjust beamwidth to cover the entire vehicle in the ISAC duration. Based on the reflected signals, the UAV can predict the state of vehicle, which is then exploited to generate tailored beams to effectively track the vehicle. To address the asymmetric sensing and communication requirements, a three-stage ISAC scheme with dynamic sensing duration and frequency is proposed according to the communication/sensing performance in real time. The initial state of vehicle is estimated in the first stage, followed by the use of ISAC wide beams in the second stage to achieve the vehicle coverage, employing an extended Kalman filtering (EKF) approach for state tracking and prediction. In the third stage, the UAV selectively transmits either an ISAC beam or a communication-only beam based on monitored sensing and communication performance metrics. Finally, simulation results are provided to evaluate the efficacy of the proposed scheme as compared to other benchmarks and also shed light on the tradeoff between communication and sensing.

The joint design of secure precoding and artificial noise (AN) transmission scheme is promising to improve secrecy performance. However, in downlink multi-user multiple-input multiple-output (MU-MIMO) systems with multiple eavesdroppers, joint design of secure precoding and AN structure involves several challenges: an objective function is non-convex and non-smooth, and a precoding matrix and AN matrix have different design principles. Classically, to jointly design precoding and AN covariance matrix, an alternating optimization approach is used which has limitations in terms of the secrecy rate performance since it does not offer joint and simultaneous optimization of the precoding and AN covariance matrices. In this paper, we propose a novel optimization framework that optimizes the precoder and the AN covariance matrix jointly and simultaneously to maximize the secrecy rate. First, we approximate the objective function to a tractable non-convex form. Next, we derive the first-order optimality condition by leveraging the nonlinear eigenvalue problem (NEP) form. Finally, we utilize an efficient technique with low computational complexity for identifying the major eigenvector of the NEP which corresponds to the best stationary point. Simulations illustrate that the proposed methods enhance the secrecy rate performance compared to the existing secure precoding methods.

Continuous authentication is essential to realize the new zero-trust based security provisioning. Conventional authentication techniques often rely on security keys, credentials, or device fingerprints, which may suffer from either high network overhead or low reliability in highly dynamic environments. To concurrently overcome these challenges, we jointly design the multi-dimensional multiple access and lightweight continuous authentication (MDMA-LCA) to explore multiple domains of the users' access channels for both communication and security enhancement. The access time frame, subchannel, and power allocation of multiple users are formulated as a joint optimization problem to maximize the achievable sum rate (ASR) of the users while continuously authenticating their identities assisted by the non-orthogonal multiple access (NOMA). The proposed scheme achieves lightweight continuous authentication by prearranging the access time sequences of multiple users and by verifying them directly and simultaneously at the base station (BS). Then, the joint optimization problem is decomposed and transferred to a maximum flow problem in a designed graph, and a joint MDMA-LCA algorithm is developed. Simulation results demonstrate that, compared with several existing schemes, the proposed scheme achieves an ASR gain while guaranteeing the continuous authentication of the users.

In this paper, we investigate a covert cooperative backscatter communication (BackCom) network under residual hardware impairments (RHIs) that are unavoidable in practical radio frequency transceivers and may have an impact on the communication covertness and reliability. By exploiting the availability of statistical channel state information, we derive analytic expressions for the eavesdropper's detection error probability (DEP) and system outage probability (SOP) to evaluate the system covertness and communication reliability, respectively. Using the derived results, we prove that RHIs can improve the communication covertness but significantly degrade the outage performance. We also show that under RHIs and extremely high transmit signal-to-noise levels, the system outage performance has a non-zero asymptote, while the DEP still approaches one, similar to the scenario with perfect hardware. Computer simulations corroborate our analytical results.

This work examines the covert communication performance gain achieved by deploying a reconfigurable intelligent surface (RIS) with discrete phase shifts. To this end, we first analyze the average receive signal power at a legitimate receiver Bob and a warden Willie as a function of the number of RIS reflecting elements$N$and the number of bits$d$for its discrete phase shift levels. Our analysis reveals that Bob's average power is proportional to$N^{2}$and highly depends on$d$, while Willie's average power is proportional to$N$and does not depend on$d$. This leads to the potential of enhancing the system performance via increasing$N$or$d$in the considered covert communications scenario. Specifically, the performance gain is explicitly examined by tackling the transmission outage probability from a transmitter Alice to Bob subject to a covertness constraint based on Willie's detection performance. After analyzing the transmission outage probability and Willie's total detection error rate, we determine Alice's optimal transmit power. Our explicit examination confirms the performance enhancement achieved via increasing$N$or$d$. Furthermore, our analysis shows that the covert communication performance achieved with$d=3$is already sufficiently close to that achieved with$d\rightarrow\infty$. This shows that the major benefits of deploying RIS in covert communications can be achieved by an RIS with low-resolution phase shifts.

To mitigate the severe large-scale fading and the energy scarcity problem in long-distance high-altitude platform (HAP) networks, in this paper, we investigate the potentials of a multi-layer refracting reconfigurable intelligent surface (RIS) -assisted receiver for enabling simultaneous wireless information and power transfer (SWIPT) in HAP networks. Unlike the existing RIS-aided reflector and transmitter, the multi-layer RIS-receiver can well overcome the severe “double fading” effect induced by the extreme long-distance HAP links and fully exploit RIS's degrees-of-freedom (DoFs) for SWIPT design. Building on the proposed RIS-receiver, this paper formulates a worst-case sum rate maximization problem under angular channel state information (CSI) imperfection, while satisfying the information rate requirements of the earth stations (ESs) and the harvested energy constraint. To handle the intractable non-convex problem, a scalable robust optimization framework utilizing the discretization method, LogSumExp-dual scheme, and modified cyclic coordinate descent (M-CCD) is proposed to obtain the semi-closed-form solutions. Numerical simulations demonstrate that the proposed architecture and optimization framework achieve superior performance with lower complexity compared with state-of-the-art schemes in HAP networks.

In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification performance of mobile devices using long term evolution (LTE) signals remains to be validated. In this paper, we propose an RFF extraction method based on LTE demodulation reference signal (DMRS) signal processing for LTE mobile devices. First, we analyze the impacts of the RFF and channel fading on DMRS in the LTE uplink channel. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of DMRS. By removing the low-frequency components of DMRS, which are mainly affected by the channel effects, our proposed method is robust to the channel impairments. Finally, our simulation and experimental results show that our method can effectively reduce the channel impacts and retain the RFF of devices. The effectiveness of this method is verified via different classification tasks. The classification accuracy can reach 98.5% and 93.9% in the stationary and mobile scenarios, respectively.

Resource allocation is of crucial importance in wireless communications. However, it is extremely challenging to design efficient resource allocation schemes for future wireless communication networks since the formulated resource allocation problems are generally non-convex and consist of various coupled variables. Moreover, the dynamic changes of practical wireless communication environment and user service requirements thirst for efficient real-time resource allocation. To tackle these issues, a novel partially observable deep multi-agent active inference (PODMAI) framework is proposed for realizing intelligent resource allocation. A belief based learning method is exploited for updating the policy by minimizing the variational free energy. A decentralized training with a decentralized execution multi-agent strategy is designed to overcome the limitations of the partially observable state information. Exploited the proposed framework, an intelligent spectrum allocation and trajectory optimization scheme is developed for a spectrum sharing unmanned aerial vehicle (UAV) network with dynamic transmission rate requirements as an example. Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes. Moreover, the convergence speed of the proposed PODMAI is significantly improved compared with the conventional reinforcement learning framework. Overall, our proposed framework can enrich the intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

A novel opportunistic semantic and bit communication strategy is proposed for uplink non-orthogonal multiple access (NOMA). Specifically, a secondary far user (F-user) employs either semantic communication (SemCom) or bit-based communication (BitCom) to participate in NOMA with a primary near user (N-user) employing the BitCom. For each fading channel state, the secondary F-user has to select the most suitable communication method, thus striking a good tradeoff between its own achieved performance and the interference imposed on the primary N-user. The optimal communication policy at the F-user over fading channels is derived for maximizing the ergodic (equivalent) semantic rate achieved at the F-user, subject to the minimum ergodic bit rate constraint of the N-user. Numerical results show that the proposed opportunistic scheme can achieve higher communication performance for NOMA than the baseline schemes merely employing SemCom or BitCom. In addition, SemCom can better guarantee the performance of the F-user admitted in NOMA than BitCom when the communication requirement of the primary N-user is high.

Recent advancement in deep learning has led to an increased interest in image processing and computer vision applications for driver monitoring systems. One of the applications where these techniques can be useful is in segmenting and tracking seatbelts. A seatbelt is an important safety feature in the vehicle that if properly used can save lives. Efficient segmentation of the seatbelts in an image provides important information about the correct use of seatbelts. The challenge in developing deep learning algorithms for seatbelt detection and segmentation is the manual annotations required for this task, which is cumbersome. This paper explores a novel formulation to efficiently train a seatbelt model with minimal supervision. We exploit the textureless and shape characteristics of the seatbelts to programmatically synthesize images. Our proposed method synthetically creates images that resemble seatbelt patterns. After training a model exclusively with synthetic images, we iteratively fine-tune it using naturalistic images extracted from online video-sharing websites. The labels for these images are pseudo-labels assigned by the model to confident predictions. Fine-tuning helps adapt the model to better work on real naturalistic images, improving the performance of the system. We obtain an F1-score of 0.55 in segmenting the seatbelt with this approach. We also experiment with fine-tuning the model with a small number of naturalistic images with annotated labels. After pretraining on synthetic samples and pseudo-labeled naturalistic images, we achieve an F1-score of 0.67 using only 200 annotated images.

We investigate a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. We design a joint optimization problem of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the sum rate of the overall network. A computationally-efficient algorithm is developed to solve the problem. Numerical results demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications.

We investigate a reconfigurable intelligent surface (RIS)-aided wireless powered mobile edge computing (MEC) network, where a new RIS-aided hybrid non-orthogonal multiple access (NOMA) offloading scheme is proposed to improve the efficiency of both MEC offloading and energy harvesting. A joint optimization framework for the transmit power, time allocation, and RIS phase shifts is developed to minimize the overall energy consumption of the network, subject to the energy causality and MEC offloading constraints. Despite of the optimization problem's non-convexity with highly coupled optimization variables, we devise a computationally-efficient algorithm to solve it iteratively. Simulation results are also provided to facilitate the performance evaluation of the RIS-aided wireless powered MEC, quantify the value of the RIS for energy efficient MEC offloading, and validate the performance gains of the proposed hybrid NOMA offloading over various baseline schemes.

This paper addresses the problem of coexistence between smart grid based wireless communication networks and other interfering signals. Particularly, the problem of interfering wireless local area network (WLAN) signals over smart utility networks (SUN) systems is analyzed. We develop a statistical model of the WLAN interferers, which is in turn used in predicting the capacity of SUN systems when affected by several WLAN interferers. Based on this analysis, a framework that promotes the application of an adaptive power control algorithm at the SUN transmitter is developed based on a Lagrangian optimization approach. Our proposed optimization approach involves solving a reduced-complexity algorithm which yields an optimal power allocation for each SUN packet. The results show that an adaptive power control approach results in a significant improvement of the SUN’s network capacity when compared with fixed power transmissions.

Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth low energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multicarrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas multiple signal classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments, including indoor, outdoor, and nonline-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.

This article studies secure communication in an Internet of Things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the nonconvex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector, and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design.

Multiple access (MA) design is investigated to facilitate the coexistence of the emerging semantic transmission and the conventional bit-based transmission in future networks. Thesemantic rateis adopted for measuring the performance of the semantic transmission. However, a key challenge is that there is no closed-form expression for a key parameter, namely thesemantic similarity, which characterizes the sentence similarity between an original sentence and the corresponding recovered sentence. To overcome this challenge, we propose a data regression method, where the semantic similarity is approximated by ageneralized logistic function. Using the obtained tractable function, we propose a heterogeneous semantic and bit communication framework, where an access point simultaneously sends the semantic and bit streams to one semantics-interested user (S-user) and one bit-interested user (B-user). To realize this heterogeneous semantic and bit transmission in multi-user networks, three MA schemes are proposed, namely orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and semi-NOMA. More specifically, the bit stream in semi-NOMA is split into two streams, one is transmitted with the semantic stream over the shared frequency sub-band and the other is transmitted over the separate orthogonal frequency sub-band. To study the fundamental performance limits of the three proposed MA schemes, thesemantic-versus-bit (SvB) rate regionand thepower regionare defined. An optimal resource allocation procedure is then derived for characterizing the boundary of the SvB rate region and the power region achieved by each MA scheme. The structures of the derived solutions demonstrate that semi-NOMA is superior to both NOMA and OMA given its highly flexible transmission policy. Our numerical results: 1) confirm that the proposed semi-NOMA is the optimal MA scheme as compared to OMA and NOMA even under the symmetric channel case, and 2) reveal that the superiority of semi-NOMA is more prominent when the channel condition of the S-user is better than that of the B-user.

With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks.

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.

Existing device authentication techniques may suffer from heavy communication, computation, and storage overhead for identifying a growing number of devices in collaborations. This paper proposes a novel group authentication (GA) method for decentralized edge collaboration by exploiting the historical collaboration process information, i.e., the distributed learning parameters and results from the previous round of collaboration. Two strategies are developed to generate tokens locally at the edge devices’ side for mutual authentication, named random token generation (R-TG) and privacy-preserving token generation (PP-TG). Specifically, the R-TG strategy randomly selects several historical learning parameters as tokens, while the PP-TG strategy designs a one-way function to defend against privacy leakage by concealing the historical information. A GA protocol is proposed, where each device simultaneously authenticates the others in the same group by repeating the learning process using their tokens. If the process converges to an expected result, all the devices are authenticated as legitimate group members at once. The proposed scheme provides a lightweight flexible solution without pre-generating and distributing any keys/secrets operating on top of a standardized security protocol, and protects the collaboration continuously. The simulation results demonstrate the viability of our scheme and its superior performance compared to several benchmark schemes.

Unmanned aerial vehicle (UAV) plays an important role in wireless communication systems, due to the additional degree of freedom realized from its flexible deployment. Driven by this advantage and considering the security issue, this paper aims to investigate UAV-enabled proactive eavesdropping over distributed transmit beamforming-based suspicious communications. Specifically, for the suspicious system, there are multiple suspicious clusters aiming to communicate with the suspicious destination (D) using mutually orthogonal frequency bands, and distributed transmit beamforming is exploited by each cluster to strengthen the signal receiving quality at D. For the legitimate party, the full-duplex UAV exploits one antenna to jam D and uses the other antenna to overhear the signals of the suspicious clusters concurrently. By resorting to the Laguerre series approximation and the central limit theorem, we first characterize, in closed form, the approximated distributions of the receiving signal-to-interference-noise ratio (SINR) at D and the UAV, which are shown to be very tight. Based on this analysis and considering that the suspicious system works in the delay-limited transmission mode or the delay-sensitive transmission mode, we aim to maximize the minimum eavesdropping success probability of the UAV for those suspicious communications links, by jointly adjusting the UAV’s deployment and jamming power allocations over different frequency bands. The problem is highly non-convex. To tackle this, we develop an alternative optimization framework and further a novel and low-complexity solution in the high SNR regime to the optimization problem. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This paper studies information surveillance over wireless-powered suspicious multiuser communications, where multiple suspicious transmitters (STs) first harvest wireless energy from the suspicious power beacon (PB) in phase I and then communicate with the suspicious destination (SD) in phase II over mutually orthogonal channels, and there is a legitimate monitor (M) aiming to overhear the suspicious signals of the STs based on wireless-powered cooperative jamming. Specifically, the jammers first harvest energy from M in phase I and then interfere with the SD in phase II. Considering the fairness issue, M aims to maximize the minimum eavesdropping success probability of these suspicious signals, by jointly optimizing its transmit power in phase I and the jammers’ power allocations in phase II. To solve the problem, first we strictly prove that M should exhaust its maximum power for the energy transfer, even the additional energy can be harvested by the STs to enhance their transmit power and rate. Then, the general successive convex approximation (SCA) technique and one low-complexity solution are respectively proposed to optimize the jammers’ power allocations. Further, the closed-form jamming power allocations are derived in the high signal-to-noise ratio range to reveal some interesting insights. The joint deployments of M and the jammers are also investigated to enhance the eavesdropping performance. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

In this paper, we propose a new simultaneous information relaying and jamming (SIRJ) scheme based on a dual-functional reconfigurable intelligent surface (RIS) to achieve secure non-orthogonal multiple access communications. Specifically, the RIS elements are split into two groups, where elements in one group perform signal reflection to enhance the legitimate reception quality while elements in the other group generate artificial jamming to interfere with the eavesdropper (Eve). Based on different channel state information (CSI) availabilities of Eve, the system sum-rate is maximized by jointly optimizing the transmit beamforming of the base station, reflect beamforming of the RIS, and mode selection of each RIS element, subject to a maximum tolerable information leakage to Eve. For the case with perfect Eve’s CSI, a penalty based alternating algorithm is proposed to deal with the challenging multivariate coupled and mixed integer non-convex optimization problem. For the case with imperfect Eve’s CSI, we consider the infinite number of secrecy constraints, for which the traditional$\mathcal {S}$-procedure cannot be directly applied. To tackle this challenge, we devise an efficient transformation that fits the$\mathcal {S}$-procedure to the problem and propose a robust secure beamforming design. Simulation results demonstrate the performance advantage of the proposed SIRJ scheme over the existing baseline schemes.

Millimeter-wave (mmWave) can provide abundant spectrum resource in vehicular communication networks. Nevertheless, due to the high path-loss and blocking effects in mmWave propagation, and high mobility of vehicles, downlink services for vehicles would be seriously degraded. In this paper, we firstly propose a deep reinforcement learning-based joint beam allocation and relay selection (JoBARS) scheme to mitigate blocking effects and optimize the total transmission rate of the vehicular network, where the mmWave base station (mmBS) provides multi-user services. When downlinks are blocked, the mmBS can select appropriate idle vehicles as relay nodes to enhance service quality from a global perspective. We set the rate punishment restriction in JoBARS scheme to guarantee each vehicle can obtain high-quality service. Besides, a relaying incentive mechanism (RIM) is proposed to avoid vehicles being overly selected for relaying and ensure that relay vehicles have a higher chance of being served in the next round. We demonstrate that JoBARS scheme can effectively enhance the total transmission rate while alleviating transmission outages caused by severe propagation attenuation of mmWave signals. Compared with Greedy Selection scheme, the total rate and average connection probability of vehicles under JoBARS scheme are nearly 17% and 14% higher when blocking effects are severe.

Unmanned aerial vehicles (UAVs) are attractive in some Internet of Things (IoT) applications, due to their flexible deployment and extended coverage. In this paper, we consider an UAV-assisted network where the UAV flies between the resource-limited sensor nodes (SNs) and collects their status updates. The UAV trajectory and SN scheduling are jointly optimized to minimize the Age of Synchronization (AoS). In contrast to the conventional Age of Information (AoI), AoS takes into account both the freshness and the content of the information, which makes AoS a more suitable design criterion for information collection in an energy-constrained wireless network. Since the formulated problem is challenging to solve due to its non convexity, we reformulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL) algorithm to obtain the optimal solution with various action and state spaces. Our simulation results show the fast convergence rate of the proposed DRL algorithm and demonstrate that our proposed scheme can improve the performance of the UAV-assisted network compared to AoI-based schemes.

In this paper, we investigate an energy efficient waveform design for integrated visible light positioning and communication (VLPC) systems by exploiting the relationship between visible light positioning (VLP) and visible light communication (VLC). We propose that the direct current component and the alternating current component of the VLPC signals are utilized for positioning and communication, respectively. With a single LED-lamp, we propose a received-signal-strength based 3D VLP scheme, and further derive the Cramer-Rao lower bound (CRLB). Then, by exploiting the inherent coupling relationship between VLP and VLC, the positioning results are utilized for channel estimation of VLC, which can significantly reduce the channel estimation pilot overhead. Furthermore, we optimize the waveform design by minimizing the CRLB, while satisfying both the outage probability of communication rate and total transmit power constraints. However, this problem turns to be non-convex and intractable. To address this challenging problem, we utilize the Conditional Value-at-Risk to conservatively transform the outage probability constraint into a deterministic form. By exploiting the block coordinate descent algorithm, the waveform design problem can be efficiently solved by alternately optimizing VLP and VLC convex sub-problems and dual problem. Finally, simulation results verify both the effectiveness and robustness of the proposed waveform design.

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

Covert transmission is investigated for a cooperative deception strategy, where a cooperative jammer (Jammer) tries to attract a multi-antenna adversary (Willie) and degrade the adversary’s reception ability for the signal from a transmitter (Alice). For this strategy, we formulate an optimization problem to maximize the covert rate when three different types of channel state information (CSI) are available. The total power is optimally allocated between Alice and Jammer subject to the Kullback-Leibler (KL) divergence constraint, which can be expressed analytically and be widely used as a covertness measurement. Different from the existing literature, in our proposed strategy, we also determine the optimal transmission power at the jammer when Alice is silent, while existing works always assume that the jammer’s power is fixed. Specifically, we apply the S-procedure to convert infinite constraints into linear-matrix-inequalities (LMI) constraints. When statistical CSI at Willie is available, we convert double integration to single integration using asymptotic approximation and substitution method. Finally, our simulation results show that for the proposed strategy, the covert rate is increased with the number of antennas at Willie. Moreover, compared to the benchmark, our proposed strategy is more robust in the presence of imperfect CSI.

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS architecture, called multi-functional RIS (MF-RIS). Different from conventional reflecting-only RIS, the proposed MF-RIS is capable of supporting multiple functions with one surface, including signal reflection, amplification, and energy harvesting. As such, our MF-RIS is able to overcome the double-fading attenuation by harvesting energy from incident signals. Through theoretical analysis, we derive the achievable capacity of an MF-RIS-aided communication network. Compared to the capacity achieved by the existing self-sustainable RIS, we derive the number of reflective elements required for MF-RIS to outperform self-sustainable RIS. To realize a self-sustainable communication system, we investigate the use of MF-RIS in improving the sum-rate of multi-user wireless networks. Specifically, we solve a non-convex optimization problem by jointly designing the transmit beamforming and MF-RIS coefficients. As an extension, we investigate a resource allocation problem in a practical scenario with imperfect channel state information. By approximating the semi-infinite constraints with the$\mathcal {S}$-procedure and the general sign-definiteness, we propose a robust beamforming scheme to combat the inevitable channel estimation errors. Finally, numerical results show that: 1) compared to the self-sustainable RIS, MF-RIS can strike a better balance between energy self-sustainability and throughput improvement; and 2) unlike reflecting-only RIS which can be deployed near the transmitter or receiver, MF-RIS should be deployed closer to the transmitter for higher spectrum efficiency.

To enhance the outage performance of downlink non-orthogonal multiple access (NOMA) systems, we investigate opportunistic user pair scheduling (UPS) as well as the joint power allocation and decoding order selection (PA-DOS) for the scheduled user pair. Unlike most existing literature where the UPS and PA-DOS schemes are solely driven by channel conditions, we consider the impact of both the channel conditions and users’ target rates in the scheme design, since these two factors jointly determine the occurrence of transmission outages. Specifically, a two-stage rate-aware scheme for joint UPS and PA-DOS is proposed, which is proved to achieve the minimum system outage probability and guarantee fair access opportunities among users. For performance evaluation, the exact system outage probability achieved by the proposed scheme is derived in closed form, based on which a high-signal-to-noise-ratio asymptotic result is further derived for meaningful insights. Our analytical results reveal a new finding: The diversity order achieved by our proposed scheme, which is also the maximum achievable diversity order of the considered NOMA system, varies with the users’ target rates. Simulations confirm our findings and demonstrate that the proposed scheme can achieve a significant outage performance gain over the existing benchmark schemes.

Integrated sensing and communication (ISAC) has recently been considered as a promising approach to save spectrum resources and reduce hardware cost. Meanwhile, as information security becomes increasingly more critical issue, government agencies urgently need to legitimately monitor suspicious communications via proactive eavesdropping. Thus, in this paper, we investigate a wireless legitimate surveillance system with radar function. We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance. The formulated problem is challenging to solve. By employing the Rayleigh quotient and fully exploiting the structure of the problem, we apply the divide-and-conquer principle to divide the formulated problem into two subproblems for two different cases. For the first case, we aim at minimizing the total transmit power, and for the second case we focus on maximizing the jamming power. For both subproblems, with the aid of orthogonal decomposition, we obtain the optimal solution of the receive and transmit beamforming vectors in closed-form. Performance analysis and discussion of some insightful results are also carried out. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithm in terms of eavesdropping success probability.

Spectrum maps are of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, existing spectrum map construction schemes mainly depend on spatial interpolation or just simply exploit the frequency correlation and cannot accurately construct the spectrum map when measurement data at the target frequency are not available. To overcome this challenge, we propose two accurate spectrum map construction schemes using different intelligent frequency-spatial reasoning methods. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct highly accurate spectrum maps of the frequencies without spectrum data by combining the joint frequency-spatial spectrum representation method with deep learning data-driven techniques. Specifically, a joint three-dimensional spectrum representation model is established and both a novel autoencoder network and a novel conditional generative adversarial network suitable for processing the three-dimensional spectrum data are proposed to realize the intelligent frequency-spatial reasoning. Simulation results demonstrate that our proposed schemes are superior to the benchmark schemes in terms of the spectrum map construction accuracy. Moreover, simulation results demonstrate that our proposed neural networks have a fast convergence speed, which achieves a better tradeoff between the computation efficiency and the construction accuracy.

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS.

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the$\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

In this paper, we propose joint beamforming and photo-detector (PD) orientation (BO) optimization schemes for mobile visible light communication (VLC) with the orientation adjustable receiver (OAR). Since VLC is sensitive to line-of-sight propagation, we first establish the OAR model and the human body blockage model for mobile VLC user equipment (UE). To guarantee the quality of service (QoS) of mobile VLC, we jointly optimize BO with minimal UE the power consumption for both fixed and random UE orientation cases. For the fixed UE orientation case, since the transmit beamforming and the PD orientation are mutually coupled, the joint BO optimization problem is nonconvex and intractable. To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation. For the random UE orientation case, we further propose a robust alternating BO optimization algorithm to ensure the worst-case QoS requirement of the mobile UE. Finally, the performance of joint BO optimization design schemes are evaluated for mobile VLC through numerical experiments.

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

This work investigates the covert information freshness in intelligent reflecting surface (IRS)-aided communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). In particular, with the help of Alice’s undisguised signal transmission, Bob can establish covert communications such that his transmission can be shielded from Willie. Considering both the non-retransmission protocol and the automatic repeat-request (ARQ) protocol for Bob’s transmission, we study the resource allocation design. By exploiting the channel statistics, the joint design of active beamforming at Alice and Bob, the passive beamforming at the IRS, and the packet length of the confidential data packet is formulated as a nonconvex optimization problem which minimizes the age of information (AoI) at Alice for the two considered protocols taking into account the quality of service in terms of the maximum tolerable AoI at Bob and communication covertness. To circumvent the non-convexity of the design problem, we propose alternating optimization algorithms to find effective solutions. Numerical results demonstrate the superiority of our proposed optimization algorithms over various benchmarks and unveil the decrease of the optimized packet length with the improved covert channel quality.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, an intelligent reflecting surface (IRS) is exploited to assist both spectrum sensing and secondary transmission in the CR network (CRN) employing opportunistic spectrum access. A novel IRS-enhanced spectrum sensing scheme and a redesigned detection threshold are proposed to improve the sensing performance. We formulate the throughput maximization problem by jointly optimizing the sensing time, the beamforming, and the IRS phase shifts. A computationally efficient block coordinate descent (BCD)-based algorithm is proposed to tackle the non-convex problem. Simulation results show that our proposed scheme is superior to other benchmark schemes in terms of both the throughput and the sensing performance.

Reconfigurable Intelligent Surface (RIS) has recently emerged as an enabling technology to enhance reliability and overcome blockage in future heterogeneous wireless networks (HetNets). Adjusting amplitudes and phases of the RIS elements to achieve such goals is a major challenge. In this paper, we study the problem of network rate control to achieve users (UEs) fairness and smallcells (SCs) load balancing in multi-cell RIS-assisted multiple-input single-output (MISO) HetNets. We consider dual-connectivity UEs that can simultaneously connect to mmWave-operating SCs and sub-6GHz-operating RIS-assisted macrocell (MC), where RISs are mainly deployed to enhance sub-6GHz signal reception and mitigate interference. Then, we formulate an optimization problem whose objective is to jointly control the active beamforming vectors of SCs and MC on the one hand and the passive beamforming vectors of RISs on the other hand to maximize UEs fairness and network load balancing. Due to the high complexity of the formulated problem, we propose a novel multi-task deep reinforcement learning (MTDRL) model based on the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the problem and learn system dynamics. Through proper definitions of network tasks and their main elements, we show via simulations that our proposed MTDRL-based model ensures fair distribution of rates within UEs and SCs and that it outperforms key benchmarks.

This paper proposes a novel concept of multi-functional reconfigurable intelligent surfaces (MF-RISs). Different from conventional single-functional RISs (SF-RISs) that only reflect signals, the proposed MF-RIS simultaneously supports multiple functionalities, namely, reflection, amplification, and energy harvesting. Specifically, by harvesting energy from incident signals, MF-RIS is able to simultaneously reflect and amplify signals without an external power supply, which is beneficial for overcoming the double-fading attenuation in a flexible manner. A new operation protocol of MF-RIS is presented, and then a sum rate (SR) maximization problem is formulated for an MF-RIS aided multi-user network. Next, an efficient iterative algorithm is proposed to solve this non-convex problem. Furthermore, through theoretical analysis, we determine the number of reflection elements required for MF-RISs to outperform self-sustainable RISs. Finally, our numerical results show that: 1) MF-RISs are able to provide up to 81.1% higher SR than the self-sustainable RISs. 2) Unlike the SF-RIS, which prefers to be deployed near the transmitter or receiver, MF-RISs should be deployed closer to the transmitter for better performance.

This article investigates intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency-division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this nonconvexity, the transmission time scheduling and the bandwidth allocation are optimally designed in the closed form by the Lagrange dual method and the Karush–Kuhn–Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose elementwise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in the closed form. We also characterize the convergence behavior of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.

Reconfigurable intelligent surfaces (RISs) and nonorthogonal multiple access (NOMA) have been recognized as key enabling techniques for the envisioned sixth generation (6G) of mobile communication networks. The key feature of RISs is to intelligently reconfigure the wireless propagation environment, which was once considered to be fixed and untunable. The key idea of NOMA is to utilize users’ dynamic channel conditions to improve spectral efficiency and user fairness. Naturally, the two communication techniques are complementary to each other and can be integrated to cope with the challenging requirements envisioned for 6G mobile networks. This survey provides a comprehensive overview of the recent progress on the synergistic integration of RISs and NOMA. In particular, the basics of both techniques are introduced first, and then, the fundamentals of RIS-NOMA are discussed for two communication scenarios with different transceiver capabilities. Resource allocation is of paramount importance for the success of RIS-assisted NOMA networks, and various approaches, including artificial intelligence (AI)-empowered designs, are introduced. Security provisioning in RIS-NOMA networks is also discussed as wireless networks are prone to security attacks due to the nature of the shared wireless medium. Finally, the survey is concluded with detailed discussions of the challenges arising in the practical implementation of RIS-NOMA, future research directions, and emerging applications.

We investigate proactive eavesdropping with one half-duplex legitimate monitor (E) in unmanned aerial vehicle (UAV)-enabled suspicious relaying systems, which consist of a suspicious transmitter (ST), a suspicious UAV-based relay (SU) and a suspicious destination (SD). Under this setup, we propose two jamming-assisted eavesdropping strategies, namely, “Eavesdrop-Then-Jam” (ETJ) and “Jam-Then-Eavesdrop” (JTE), to maximize the eavesdropping throughput of E. Specifically, for ETJ, E first eavesdrops the suspicious signal of the ST in the ST-SU phase and then jams the SD in the SU-SD phase; for JTE, E first jams the SU in the ST-SU phase and then eavesdrops the suspicious signal of the SU in the SU-SD phase.However, in both strategies, as the jamming power of E (independent variable) changes, the SU can also adaptively adjust its deployment (dependent variable) to maximize the benefit of the suspicious system. Facing this challenge, we can only employ the undesirable exhaustive search over both the independent and dependent variables to achieve the optimal solution. To decrease the complexity, we further derive a tight approximation of the Lambert function and then develop easy-to-implement algorithms to find the sub-optimal solutions. Numerical results demonstrate the effectiveness of our proposed strategies compared to conventional passive eavesdropping.

This letter studies jamming-assisted proactive eavesdropping over multiple orthogonal suspicious links with a wireless-powered monitor. Considering the shortage of energy, the monitor adopts the power splitting technique to divide each of the received suspicious signals into two parts for information decoding and energy harvesting, and then exploits the accumulated energy to jam over those suspicious links, to reduce the corresponding suspicious communication rate and facilitate the eavesdropping of the monitor itself. Our objective is to maximize the minimum eavesdropping success probability of the monitor for all received suspicious signals, by jointly optimizing its power splitting ratios and jamming power allocations. The formulated problem involves the complex Lambert function and is highly non-convex. To tackle this challenge, first we derive a very tight approximation of the Lambert function based on its key property. Then, we employ the general successive convex approximation (SCA)-based technique to iteratively find a locally optimal solution. Moreover, we propose a parallel coordinate descent (PCD)-based low-complexity algorithm to obtain a sub-optimal solution. Numerical results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This letter studies proactive eavesdropping with one legitimate monitor (E) in the classical single-hop suspicious communication. Specifically, unlike all previous works that ignored the suspicious channel training phase and just considered the jamming power optimization of E in the suspicious data transmission phase to facilitate eavesdropping, this letter advances the research by comprehensively investigating the jamming power allocation of E in both phases, with the purpose of maximizing its eavesdropping success probability. Under this setup, we first derive an exact expression of the objective and reveal the existence of a fundamental trade-off in deciding the jamming power allocation, for which a simple one-dimensional search is employed to find the optimal solution. To simplify the analysis, we further derive a tight approximation of the objective and then develop a very fast alternating optimization algorithm to find the sub-optimal jamming power allocation. Moreover, we extend our analysis to the case where the channel state information is available at the suspicious transmitter via channel feedback. Simulation results demonstrate the effectiveness of our proposed scheme compared to competitive benchmarks.

The promising physical-layer authentication (PLA) scheme enjoys low computational complexity and provides a lightweight solution for the wireless transmission security problem. However, the ideal but impractical assumptions are made on the priori knowledge in the traditional PLA schemes, which fail because the priori knowledge can be interfered by the impersonation attacks. Hence, a blind PLA scheme featured by the composite radio sample characteristics, active monitoring slots, and blind hypotheses tests is proposed in this paper. In particular, the intrinsic location-specific channel response integrated with the transmitter-specific signal knowledge is sampled during the active monitoring slots and is used to conduct minimum error (ME) and Neyman-Pearson (NP) hypothesis tests. The authentication reliability is enhanced significantly by jointly using the ME and NP criteria. For both the stationary and the non-stationary signal cases, our theoretical analysis shows that the temporal channel variations, the spatial channel correlations and the spectral bandwidth contribute to improve the authentication reliability. Our simulation results not only demonstrate the effectiveness of the proposed PLA scheme, but also indicate the possible scenarios in which ME outperforms NP, and NP outperforms ME.

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver’s location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

We investigate covert wireless communication in a multi-antenna relay network, where the relay transmits its own covert message to the destination when assisting the source’s information delivery, and the source acts as a warden to detect this covert transmission. Based on whether the channel state information of the relay-destination link is available at the source or not, we propose two relay beamforming schemes, namely random beamforming and maximum-ratio transmission (MRT) beamforming schemes, to guarantee the reception reliability at the destination while deliberately introducing uncertainty to the source to degrade its detection. Under the worst-case covert communication scenario where the source is capable of optimizing its detection threshold, analytical expressions for the minimum detection error probability achieved by each of the proposed schemes are derived to evaluate the detection limits of the source. By utilizing the above analytical results as the covertness constraint, an optimization problem of transmit power allocation for each scheme is formulated and solved to maximize the covert rate. The impact of imperfect channel state information on the covert communication performance is also examined. Simulation results are performed to confirm the accuracy of the derived analytical results and quantify the communication covertness enhancement of the proposed schemes. Our results also show that the MRT beamforming scheme offers a higher covert rate than that of the random beamforming scheme, especially when the covertness constraint becomes loose and/or the number of antennas at the relay increases.

In this paper, we investigate the optimal probabilistic constellation shaping design for covert communication systems from a practical view. Different from conventional covert communications with equiprobable constellations modulation, we propose non-equiprobable constellations modulation schemes to further enhance the covert rate. Specifically, we derive covert rate expressions for practical discrete constellation inputs for the first time. Then, we study the covert rate maximization problem by jointly optimizing the constellation distribution and power allocation. In particular, an approximate gradient descent method is proposed for obtaining the optimal probabilistic constellation shaping. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes a lower bound on the achievable rate where the optimal probabilistic constellation shaping problem can be solved efficiently using the Frank-Wolfe method. Extensive numerical results show that the optimized probabilistic constellation shaping strategies provide significant gains in the achievable covert rate over the state-of-the-art schemes.

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behavior and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions, and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents themultimodal driver monitoring(MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi-Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver’s facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver’s activities are recorded with high definition RGB cameras and a time-of-flight depth camera. We also record thecontroller area network-bus(CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob’s covert transmission from a warden (Willie), which cost-effectively exploits the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.

Considering multi-user downlink ultra-high reliability and low latency communications (URLLC), this paper employs the artificial noise (AN) technique to establish a secure and covert broadcast communication paradigm for the first time. Specifically, a multi-antenna transmitter (Alice) broadcasts the confidential information to multiple legitimate users in the presence of a multi-antenna malicious warden (Willie) and a multi-antenna eavesdropper (Eve). It is well known that AN is an effective technique for securing the physical layer security (PLS) of signal transmissions. Nevertheless, AN emission also exposes the signal transmission and decreases the signal covertness. Taking into account the impact of short-packet URLLC transmissions, we investigate the joint optimization of the precoder and AN to maximize the secrecy rate under the covertness constraint. Although the considered problem is nonconvex, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve it optimally. However, the nested-loop structure of the BRB-based algorithm incurs a high computational complexity. To strike a balance between the performance and computational complexity, we also propose a low-complexity penalty successive convex approximation (SCA)-based algorithm, whose performance approaches that of the optimal BRB-based algorithm, particularly in the low to medium transmit power regime. Simulation results demonstrate the excellent performance of our proposed optimization algorithms compared with various benchmark algorithms and unveil the importance of exploiting AN for secrecy provisioning.

This paper investigates intelligent reflecting surface (IRS)-assisted systems, where an access point sends independent information to multiple users with the aid of one IRS. Our goal is to characterize the capacity region of the IRS-assisted multiuser communication systems. We jointly optimize the discrete phase-shift matrix of the IRS and resource allocation with the capacity-achieving non-orthogonal multiple access (NOMA) transmission scheme. The Pareto boundary of the capacity region is characterized by maximizing the average sum rate of all users, subject to a set of rate-profile constraints, total transmit power and discrete IRS phase shift constraints. Though the formulated problem is non-convex, we derive the globally optimal solutions by invoking the Lagrange duality method. It is shown that the optimal transmission strategy is alternating transmission among different user groups by dynamically adjusting the IRS phase shifts. We further propose a Hadamard codebook based scheme, which serves as a lower bound on the optimal performance gains. Numerical results demonstrate that: i) the IRS is capable of significantly improving the capacity region; ii) the capacity region achieved by the Hadamard codebook based scheme is close to that of discrete phase shifts for a small number of IRS elements.

This paper proposes a secure amplify-and-forward (AF) relaying scheme in unmanned aerial vehicle (UAV) networks while accounting for the power consumption limitations of UAVs. Since a UAV's battery life is limited, we propose selecting the UAV relays based on their probability of availability, which we model to be a function of the UAV's power consumption model. Then, physical layer security is achieved by partitioning the limited available relays into information relaying UAVs and cooperative jammers while aiming to maximize the secrecy rate of the network when an eavesdropper is located in the vicinity of the destination node. Meanwhile, the information bearing relays use beamforming to enhance the information delivery to the destination, and the cooperative jamming relays use precoded artificial-noise scheme to degrade the eavesdropping links' received signal-to-noise ratios. Simulation results show significant secrecy rate enhancements when the proposed schemes are adopted compared to conventional relaying scenarios, especially when cooperative jamming is deployed.

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS.

Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost.

This paper considers a two-user downlink transmission in intelligent reflecting surface (IRS) aided network over fading channels. Particularly, non-orthogonal multiple access (NOMA) and two orthogonal multiple access (OMA) schemes, namely, time division multiple access (TDMA) and frequency division multiple access (FDMA), are studied. The objective is to maximize the system average sum rate for the delay-tolerant transmission. We propose two adjustment schemes, namely, dynamic phase adjustment and one-time phase adjustment. The power budget, minimum average data rate, and discrete unit modulus reflection coefficient are considered as constrains. To solve the problem, two phase shifters adjustment algorithms with low complexity are proposed to obtain near optimal solutions. With given phase shifters and satisfaction of time-sharing condition, the optimal resource allocations are obtained using the Lagrangian dual decomposition. The numerical results reveal that: i) the average sum rate of proposed NOMA network aided by IRS outperforms the conventional NOMA network over fading channels; ii) with continuous IRS adjustment in the fading block, the proposed TDMA scheme performs better than the FDMA scheme; iii) increasing the minimum average user rate requirement has less impact on the proposed IRS-NOMA system than on the IRS-OMA system.

In this paper, we investigate secrecy-energy efficient hybrid beamforming (BF) schemes for a satellite-terrestrial integrated network, wherein a multibeam satellite system shares the millimeter wave spectrum with a cellular system. Under the assumption of imperfect angles of departure for the wiretap channels, the hybrid beamformer at the base station and digital beamformers at the satellite are jointly designed to maximize the achievable secrecy-energy efficiency, while satisfying signal-to-interference-plus-noise ratio constraints of both the earth stations (ESs) and cellular users. Since the formulated optimization problem is nonconvex and mathematically intractable, we propose two robust BF schemes to obtain approximate solutions with low complexity. Specifically, for the case of a single ES, we integrate the Charnes-Cooper approach with an iterative search algorithm to convert the original nonconvex problem into a solvable one and obtain the BF weight vectors. In the case of multiple ESs, by exploiting the sequential convex approximation method, we convert the original problem into a linear one with multiple matrix inequalities and second-order cone constraints, for which we obtain a solution with satisfactory performance. The effectiveness and superiority of the proposed robust BF design schemes are validated via simulations using realistic satellite and terrestrial downlink channel models.

The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this article. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized by jointly optimizing the IRS reflection matrix and wireless resource allocation under the constraints of a maximum number of IRS reconfiguration times. In NOMA, all users are served in the same resource blocks by employing superposition coding and successive interference cancelation techniques. In OMA, all users are served by being allocated orthogonal resource blocks of different sizes. For NOMA, the ideal case with an asymptotically large number of IRS reconfiguration times is firstly considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shift at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie's link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One local optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie's link, the optimization problem is reformulated by using the triangle and Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alternative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The numerical results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob.

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves a joint optimization of NOMA user partitioning and RIS phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. To solve the challenging joint optimization problem, we invoke a modified object migration automation (MOMA) algorithm to partition the users into equal-size clusters. To optimize the RIS phase shifting matrix, we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Different from conventional training-then-testing processing, we consider a long-term self-adjusting learning model where the intelligent agent is capable of learning the optimal action for every given state through exploration and exploitation. Extensive numerical results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves enhanced sum data rate compared with the conventional orthogonal multiple access (OMA) framework. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy in a long-term manner. 3) The performance of the proposed RIS-aided NOMA framework can be improved by increasing the granularity of the RIS phase shifts. The numerical results also show that increasing the number of reflecting elements (REs) is an efficient method to improve the sum data rate of the MUs.

This paper considers the downlink of reconfigurable intelligent surface (RIS) assisted cooperative non-orthogonal multiple access (CNOMA) systems. Our objective is to minimize the total transmit power by jointly optimizing the active beamforming vectors, transmit-relaying power, and RIS phase shifts. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem. To tackle this problem, the alternating optimization approach is utilized to decouple the variables. In each alternative procedure, the optimal solutions for the active beamforming vectors, transmit-relaying power and phase shifts are obtained. However, the proposed algorithm has high complexity since the optimal phase shifts are solved by integer linear programming (ILP) whose computational complexity is exponential in the number of variables. To strike a good computational complexity-optimality trade-off, a low-complexity suboptimal algorithm is proposed by adopting the iterative penalty function based semidefinite programming (SDP) and the successive refinement approaches. Numerical results illustrate that: i) the proposed RIS-CNOMA system, aided by our proposed algorithms, outperforms the conventional CNOMA system. ii) the proposed low-complexity suboptimal algorithm can achieve near-optimal performance. iii) whether the RIS-CNOMA system outperforms the RIS assisted non-orthogonal multiple access (RIS-NOMA) system depends not only on the users’ locations but also on the RIS’s location.

In this paper, we consider a common unicast beamforming network where Alice utilizes the communication to Carol as a cover and covertly transmits a message to Bob without being recognized by Willie. We investigate the beamformer design of Alice to maximize the covert rate to Bob when Alice has either perfect or imperfect knowledge about Willie's channel state information (WCSI). For the perfect WCSI case, the problem is formulated under the perfect covert constraint, and we develop a covert beamformer by applying semidefinite relaxation and the bisection method. Then, to reduce the computational complexity, we further propose a zero-forcing beamformer design with a single iteration processing. For the case of the imperfect WCSI, the robust beamformer is developed based on a relaxation and restriction approach by utilizing the property of Kullback-Leibler divergence. Furthermore, we derive the optimal decision threshold of Willie, and analyze the false alarm and the missed detection probabilities in this case. Finally, the performance of the proposed beamformer designs is evaluated through numerical experiments.

This paper considers a two-user downlink intelligent reflecting surface (IRS) assisted network over fading channels. Particularly, non-orthogonal multiple access (NOMA) and two orthogonal multiple access (OMA) schemes, namely, time division multiple access (TDMA) and frequency division multiple access (FDMA), are studied. Our goal is maximizing the system average sum rate for delay-tolerant transmission. The power budget, minimum average user rate and discrete unit modulus reflection coefficient constraints are considered as constraints. To solve the problem, phase shifters adjustment algorithms with low complexity are proposed to obtain high quality solutions. With given phase shifters, the optimal power allocation is obtained using the Lagrangian dual decomposition. Numerical results demonstrate the performance gain by introducing IRS into the system as well as the utility of the proposed algorithms.

In this paper, we propose a novel robust downlink beamforming (BF) design for satellite-terrestrial integrated networks. Under a realistic assumption that the angular information of eavesdroppers is not perfectly known, we establish an optimization framework for hybrid BF at the terrestrial base station and digital BF at the satellite to maximize the secrecy-energy efficiency of the system, while satisfying the quality-of-service constraints of both earth station and cellular user. Since the formulated optimization problem is mathematically intractable, we present an iterative algorithm based on the Charnes-Cooper approach to optimize the BF weight vectors. The effectiveness and superiority of the proposed robust hybrid BF scheme are validated via computer simulations.

In this paper, we investigate a NOMA assisted multi-antenna two-way relay system, where users apply NOMA to support bidirectional superposition transmission with the aid of multiple relays. Specifically, we propose a multiple-access broadcast NOMA strategy together with a joint antenna-and-relay selection scheme to enhance its transmission reliability. Analytical closed-form expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed strategy with the joint antenna-and-relay selection scheme. Based on the analytical result, we further optimize the power allocation to reduce the outage probability. Our numerical results show that the proposed mechanism significantly outperforms existing benchmark strategies in terms of the outage probability.

Secrecy transmission is investigated for a cooperative jamming scheme, where a multi-antenna jammer generates artificial noise (AN) to confuse eavesdroppers. Two kinds of eavesdroppers are considered: passive eavesdroppers who only overhear the legitimate information, and active eavesdroppers who not only overhear the legitimate information but also jam the legitimate signal. Existing works only treat the passive and active eavesdroppers separately. Different from the existing works, we investigate the achievable secrecy rate in presence of both active and passive eavesdroppers. For the considered system model, we assume that the instantaneous channel state information (CSI) of the active eavesdroppers is available at the jammer, while only partial CSI of the passive eavesdroppers is available at the jammer. A new zero-forcing beamforming scheme is proposed in the presence of both active and passive eavesdroppers. For both the perfect and imperfect CSI cases, the total transmission power allocation between the information and AN signals is optimized to maximize the achievable secrecy rate. Numerical results show that imperfect CSI between the jammer and the legitimate receiver will do more harm to the achievable secrecy rate than imperfect CSI between the jammer and the active eavesdropper.

Rate splitting (RS) has the potential to significantly enhance both energy efficiency (EE) and spectral efficiency (SE) of wireless networks. In this paper, we propose joint design of the beamforming and rate allocation to maximize both EE and SE of a downlink multicell multiple-input single-output (MISO) system with rate splitting and common beamforming coordination (RS-CBC). This design problem is formulated as a non-convex quadratically-constrained multi-objective optimization problem (MOOP). By investigating the quasi-concavity relationship between EE and SE, the formulated MOOP is transformed into a single-objective optimization problem (SOOP) to offer a tradeoff between EE and SE by maximizing EE in any achievable SE region. A series of transformations are then applied to make the SOOP tractable, after which an efficient iterative algorithm based on successive convex approximation (SCA) is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and unveil interesting tradeoffs between EE and SE under different parameter settings.

This article investigates the downlink communications of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. To maximize the system throughput, we formulate a joint optimization problem over the channel assignment, decoding order of NOMA users, power allocation, and reflection coefficients. The formulated problem is proved to be NP-hard. To tackle this problem, a three-step novel resource allocation algorithm is proposed. Firstly, the channel assignment problem is solved by a many-to-one matching algorithm. Secondly, by considering the IRS reflection coefficients design, a low-complexity decoding order optimization algorithm is proposed. Thirdly, given a channel assignment and decoding order, a joint optimization algorithm is proposed for solving the joint power allocation and reflection coefficient design problem. Numerical results illustrate that: i) with the aid of IRS, the proposed IRS-NOMA system outperforms the conventional NOMA system without the IRS in terms of system throughput; ii) the proposed IRS-NOMA system achieves higher system throughput than the IRS assisted orthogonal multiple access (IRS-OMA) systems; iii) simulation results show that the performance gains of the IRS-NOMA and the IRS-OMA systems can be enhanced via carefully choosing the location of the IRS.

In this paper, we investigate a non-orthogonal multiple access (NOMA) assisted multi-antenna two-way relay system, where multi-antenna users apply NOMA to support bidirectional superposition transmission via multiple multi-antenna relays. Specifically, we propose two cooperative strategies, namely multiple-access broadcast NOMA and time division broadcast NOMA. For each of the two cooperative strategies, we devise a joint antenna-and-relay selection scheme to enhance the transmission reliability. Analytical expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed cooperative strategies with the corresponding joint antenna-and-relay selection schemes. To further reduce the outage probability, we use the derived analytical results as objective functions to optimize the transmit power allocation under both cooperative strategies. Finally, extensive simulations are carried out to validate the accuracy of the derived analytical results. Our simulation results indicate that the proposed strategies significantly outperform existing benchmark strategies in terms of outage probability and diversity order.

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifts and discrete phase shifts. To tackle the passive beamforming problem with continuous phase shifts, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifts. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with the proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-aided NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system.

A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge computing (MEC) framework is proposed. For the purpose of efficiently allocating communication and computation resources to users' computation tasks requests, we propose a long-short-term memory (LSTM) network to predict the task popularity. Based on the predicted task popularity, a long-term reward maximization problem is formulated that involves a joint optimization of the task offloading decisions, computation resource allocation, and caching decisions. To tackle this challenging problem, a single-agent Q-learning (SAQ-learning) algorithm is invoked to learn a long-term resource allocation strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions. More specifically, a BLA based action select scheme is proposed for the agents in MAQ-learning to select the optimal action in every state. We prove that the BLA based action selection scheme is instantaneously self-correcting and the selected action is an optimal solution for each state. Extensive simulation results demonstrate that: 1) The prediction error of the proposed LSTMs based task popularity prediction decreases with increasing learning rate. 2) The proposed framework significantly outperforms the benchmarks like all local computing, all offloading computing and non-cache computing. 3) The proposed BLA based MAQ-learning achieves an improved performance compared to conventional MAQ-learning algorithm.

Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Different from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is difficult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.

An effective approach to enhance the data rate in narrowband power line communication (NBPLC) system is multicarrier modulation based on orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) transmission over multiple power line phases. A key challenge for achieving reliable communication over MIMO-OFDM NBPLC is to mitigate the effects of the correlated non-stationary additive noise. In fact, substantial components of the noise in NBPLC systems exhibit a cyclostationary behavior with a period of half the AC cycle. Moreover, when MIMO transmission is adopted, an important issue that must be considered is the cross-correlation between the different phases. In this work, we propose to classify the cyclostationary noise into three classes, based on the evaluation of second order statistics. In addition, we derive estimates of the probability density functions for each of the three classes and show that while two of them exhibit a Gaussian behavior, the third one has an impulsive behaviour similar to the Middleton class-A noise. Simulation results show that the bit error rate (BER) of MIMO-OFDM NBPLC significantly changes between different classes of noise. Hence, we develop an algorithm for switching data delivery between MIMO-OFDM NBPLC and MIMO-OFDM wireless transmission in unlicensed frequency band that takes into account knowledge of the periodicity of the three classes of noises. The result is a hybrid MIMO-OFDM wireless/NBPLC system, which we refer to as, hybrid MIMO-OFDM WiNPLC. Our simulation results demonstrate BER improvement of the proposed hybrid system over individual MIMO-OFDM NBPLC or MIMO-OFDM wireless systems.

With the significant increase of wireless network nodes and traffic load in recent years, especially in the emerging internet-of-things (IoT) and vehicular networks, the design of a fast adaptive relay selection algorithm that is able to cope with a quickly changing environment became a necessity. In particular, the problem of multiple relay selection and beamforming under individual power constraints is investigated in this paper when the amplify-and-forward protocol is used to forward the data to the destination. The proposed algorithm first performs relay selection and beamforming using iterative convex optimization. The selection decisions are stored and processed before being used by a proposed multi-agent machine-learning (ML) model to imitate with high accuracy the optimal selection decision in real time with much less computational complexity. Simulation results confirm that the performance of the proposed technique is very close to the exhaustive search (ES) and to well known algorithms but with an execution time that is thousands of times shorter than traditional techniques.

We propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. The proposed framework considers opportunistic spectrum sensing integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, average energy consumption and energy efficiency in the considered set up. In addition, we formulate an optimization problem to maximize the energy efficiency of the CR system operating in mixed ABC- and HTT-modes, subject to primary interference and energy harvesting constraints. Next, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, and the optimal degree of tradeoff between the CR system operating in the ABC- and HTT-modes. We present extensive numerical results to corroborate our analysis and to demonstrate the performance gain of the proposed model in terms of energy efficiency.

This IEEE Journal on Selected Areas in Communications (JSAC) special issue (SI) aims to provide a comprehensive overview of the state-of-the-art advances and a view of emerging research challenges and opportunities forSpatial Modulation in Emerging Wireless Systems. This SI solicits high-quality original research papers regarding theoretical studies, and application-oriented contributions dealing with architectures, platforms, and multiple access schemes.

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

We propose a novel non-orthogonal multiple access (NOMA)-inspired jamming and relaying scheme to enhance the physical layer security of untrusted relay networks. Particularly, during the first phase, with the application of the downlink NOMA principle, the source sends a superimposed version of a desired signal and a jamming signal, where the jamming signal is designed to deliberately confuse the untrusted relay by exploiting the beamforming design and adapting the transmission rate at the source. During the second phase, relying on the uplink NOMA principle, the untrusted relay forwards its received signals and, simultaneously, the source transmits a new desired signal that cannot be wiretapped by the untrusted relay to maximize the secrecy sum rate. Scenarios of single-antenna and multiple-antenna relaying are considered, and the impact of different antenna configurations at the source and the untrusted relay on the secrecy performance is investigated. Analytical expressions of an ergodic secrecy sum rate (ESSR) lower bound and an asymptotic ESSR scaling law are derived to evaluate the secrecy performance of the proposed NOMA-inspired jamming and relaying scheme. Computer simulations are presented to validate the accuracy of the derived analytical results, and demonstrate the significant ESSR improvement of the proposed scheme over the conventional orthogonal multiple access-based relaying schemes.

This paper investigates a secure wireless-powered integrated service system with full-duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programming-based two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel-state information case, where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO schemes simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. The simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.

Previous approaches for blind identification of space-frequency block codes (SFBCs) do not perform well for short observation periods due to their inefficient utilization of frequency-domain redundancy. This paper proposes a hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based algorithm for the SFBC signals' identification over frequency-selective fading channels to exploit two-dimensional space-frequency domain redundancy. Based on the central limit theorem, space-domain redundancy is used to construct the cross-correlation function of the estimator and frequency-domain redundancy is incorporated in the construction of the statistics. The difference between two proposed algorithms is that the HT-based algorithm constructs a chi-square statistic and employs an HT to make the decision, while the SVM-based algorithm constructs a non-central chi-square statistic with unknown mean as a strongly distinguishable statistical feature and uses SVM to make the decision. Both the algorithms do not require knowledge of the channel coefficients, modulation type, or noise power, and the SVM-based algorithm does not require timing synchronization. The simulation results verify the superior performance of the proposed algorithms for short observation periods with comparable computational complexity to conventional algorithms, as well as their acceptable identification performance in the presence of transmission impairments.

In this paper, we investigate the tradeoff between security and throughput and between security and queuing delay in wiretap time-division multiple access (TDMA) networks. We derive a simple relationship, characterized by a single key system parameter, between the stable-throughput region, where there are no perfect secrecy constraints on the data transmissions, and the secure stable-throughput region, where there are perfect secrecy constraints. We quantify the impact of the perfect secrecy constraints on the network's average queuing delay and propose a novel cross-layer security scheme for delay-limited applications. We establish an insightful link between computational security (i.e., upper-layer security implemented through cryptographic schemes) and physical-layer (information-theoretically proved) security. For the two-user case, we derive a closed-form expression for the network's minimum average queuing delay under the proposed security scheme and provide a relationship between the network's minimum queuing delay under perfect secrecy constraints and computational-only secrecy constraints. Moreover, we investigate the impact of cooperative jamming on achieving perfect secrecy, minimum network's queuing delay, and maximum throughput. We verify our theoretical findings through simulations.

We investigate the physical layer security of wireless single-input single-output orthogonal-division multiplexing (OFDM) when a transmitter, which we refer to as Alice, sends her information to a receiver, which we refer to as Bob, in the presence of an eavesdropping node, Eve. To prevent information leakage, Alice sends an artificial-noise (AN) signal superimposed over her information signal. We investigate the impact of the channel delay spread, OFDM cyclic prefix, information/AN power allocation, and information and AN precoders design on the achievable average secrecy rate. We consider the two cases of known and unknown channel state information (CSI) at Alice. Furthermore, we compare both cases of per-sub-channel processing and joint sub-channels processing at Eve's receiver. Our numerical results show the gains of AN injection in terms of average secrecy rate for different OFDM operating conditions. Moreover, based on our new insights, we demonstrate that the AN-aided scheme is effective and achieves almost the same average secrecy rate as the full-CSI case without the need for Eve's instantaneous CSI at Alice.

We propose a new scheme to enhance the physical-layer security of wireless single-input single-output orthogonal-frequency division-multiplexing (OFDM) transmissions from an electric vehicle, Alice, to the aggregator, Bob, in the presence of an eavesdropper, Eve. To prevent information leakage to Eve, Alice exploits the wireless channel randomness to extract secret key symbols that are used to encrypt some data symbols which are then multiplexed in the frequency domain with the remaining unencrypted data symbols. To secure the unencrypted data symbols, Alice transmits an artificial-noise (AN) signal superimposed over her data signal. We propose a three-level optimization procedure to increase the average secrecy rate of this wiretap channel by optimizing the transmit power allocation between the encrypted data symbols, unencrypted data symbols and the AN symbols. Our numerical results show that the proposed scheme achieves considerable secrecy rate gains compared to the benchmark cases.

We investigate the energy efficiency of a conventional collaborative compressed sensing (CCCS) scheme in cognitive radio networks. In particular, we derive expressions for the throughput, energy consumption and energy efficiency, and analyze the trade-off between the achievable throughput and the energy consumption of the underlying CCCS scheme. Furthermore, we formulate a multiple variable non-convex optimization problem to determine the optimum compression level that maximizes the energy efficiency, subject to interference constraints. We propose a sub-optimal solution based on tight approximations to simplify the aforementioned optimization problem, and further demonstrate that the energy efficiency achieved by the CCCS scheme is higher than that of conventional collaborative sensing scheme, under the same predefined conditions. It is further shown that the increase in the energy efficiency of CCCS scheme is due to the considerable decrease in the energy consumption, which is particularly noticeable with a large number of sensors.

Security is a critical issue in full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems with loopback self-interference cancellation. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a secrecy rate maximization problem under energy transfer rate constraints. Although the secrecy rate maximization problem is non-convex, we solve it via semidefinite relaxation and a two-dimensional search. We prove the optimality of our proposed algorithm and demonstrate its performance via simulations.

The cyclostationary noise in low-voltage narrowband powerline communications (LV NB-PLC) severely degrades the system's reliability. In this paper, we adopt single-input multi-output (SIMO) transmission to enhance the reliability of NB-PLC. In addition to SIMO diversity and power gains, another SIMO advantage in the LV NB-PLC is the high cyclostationary noise correlation observed over the different receive phases which can significantly help in noise mitigation. Considering the SIMO receiver structure, we propose a temporal-region-based cyclostationary noise mitigation technique that minimizes the mean-squared error in estimating the frequency- domain information signal. The proposed technique exploits the cyclostationarity of the noise to estimate its power spectral density as well as the cross-correlation per frequency subchannel over multiple stationary noise temporal regions. Our proposed SIMO NB-PLC noise mitigation technique is shown via simulation results conducted using field noise measurements to achieve considerable performance gains over single-input single-output noise mitigation techniques and over the conventional SIMO maximal-ratio-combiner designed assuming stationary noise.

We investigate the physical-layer security of indoor hybrid parallel power-line/wireless orthogonal-frequency division-multiplexing (OFDM) communication systems. We propose an artificial- noise (AN) aided scheme to enhance the system's security in the presence of an eavesdropper by exploiting the decoupled nature of the power-line and wireless communication media. The proposed scheme does not require the instantaneous channel state information of the eavesdropper's links to be known at the legitimate nodes. In our proposed scheme, the legitimate transmitter (Alice) and the legitimate receiver (Bob) cooperate to secure the hybrid system where an AN signal is shared from Bob to Alice on the link with the lower channel- to-noise ratio (CNR) while the information stream in addition to a noisy-amplified version of the received AN signal is transmitted from Alice to Bob on the link with higher CNR at each OFDM sub- channel. In addition, we investigate the effect of the transmit power levels at both Alice and Bob and the power allocation ratio between the data and AN signals at Alice on the secure throughput. We investigate both single-link eavesdropping attacks, where only one link is exposed to eavesdropping attacks, and two-link eavesdropping attacks, where the two links are exposed to eavesdropping attacks.

In this paper, we investigate a trade-off between energy consumption and physical layer security in a dual-hop untrusted multiple amplify-and-forward relaying network. Considering the presence of the direct link, we propose an adaptive switching scheme between the direct and the relaying modes. Since relay nodes are energy-constrained, the direct link is prioritized. With outdated channel state information (CSI), to save energy while forwarding the source information, we adopt a greedy relay selection scheme and derive a tight upper bound on the secrecy outage probability. We quantify the impact of outdated CSI on the secrecy performance and the secrecy gains of our proposed mode switching scheme over its fixed transmission counterparts.

In-phase/quadrature-phase Imbalance (IQI) is considered a major performance-limiting impairment in direct-conversion transceivers. Its effects become even more pronounced at higher carrier frequencies such as the millimeter-wave frequency bands considered for 5G systems. In this work, we quantify the effects of IQI on the performance of different modulations under multipath fading channels. This is realized by developing a comprehensive framework for the symbol error rate (SER) analysis of coherent phase shift keying (PSK), noncoherent differential phase shift keying (DPSK) and noncoherent frequency shift keying (FSK) under IQI effects. In this context, the moment generating function of the signal-to-interference-plus-noise-ratio is first derived for single-carrier systems suffering from transmitter (TX) IQI only, receiver (RX) IQI only and joint TX/RX IQI. Capitalizing on this, we derive analytic expressions for the SER of the different modulation schemes considered. These expressions are corroborated with simulation results and they provide insights into the dependence of IQI on the system parameters. We further demonstrate that, while in some cases, IQI can cause a slight degradation of the SER performance and, hence, it can be neglected, in other cases it should be compensated in order to achieve a reliable communication link.

Joint data and off-grid narrow-band interference (NBI) recovery is investigated in orthogonal-frequency-division-multiplexing (OFDM) systems using compressive-sensing (CS) framework. The joint recovery problem is formulated as a convex optimization problem of three weighted norms. A reduced computational complexity and scalable algorithm is proposed to solve the recovery problem based on the alternating-direction-method-of-multipliers (ADMM). Simulation results, show that the average-run-time of solving the joint recovery problem using the proposed ADMM algorithm is much less than the average-run-time of the interior point method.

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that has the potential to improve the spectral efficiency of fifth generation (5G) wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio-frequency (RF) front-end related impairments that can lead to non-negligible degradation of the overall system performance. In this context, in-phase/quadrature-phase imbalance (IQI) constitutes a major impairment in direct-conversion transceivers. Based on this, the present contribution quantifies the effects of IQI on the performance of NOMA based systems under multipath fading conditions. This is realized by first deriving novel analytic expressions for the signal-to-interference-plus-noise ratio and the outage probability of NOMA systems subject to IQI at the transmitter and/or the receiver sites. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably between the different NOMA users and depending on the considered system's parameters.

The performance of physical-layer security of the classic Wyner's wiretap model over Fisher-Snedecor F composite fading channels is considered in this work. Specifically, the main channel (i.e., between the source and the legitimate destination) and the eavesdropper's channel (i.e., between the source and the illegitimate destination) are assumed to experience independent quasi-static Fisher-Snedecor F fading conditions, which have been shown to be encountered in realistic wireless transmission scenarios in conventional and emerging communication systems. In this context, exact closed-form expressions for the average secrecy capacity (ASC) and the probability of non-zero secrecy capacity (PNSC) are derived. Additionally, an asymptotic analytical expression for the ASC is presented. The impact of shadowing and multipath fading on the secrecy performance is investigated. Our results show that increasing the fading parameter of the main channel and/or the shadowing parameter of the eavesdropper's channel improves the secrecy performance. The analytical results are compared with Monte-Carlo simulations to validate the analysis.

Millimeter-wave non-orthogonal multiple access (mm-wave-NOMA) systems exploit the power domain for multiple accesses to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mm-wave systems. This paper investigates the sum rate maximization problem of mm-wave-NOMA systems under the constraints of the total transmission power and users' predefined rate requirements. The formulated optimization problem is a non-linear programming problem and, thus, is non-convex and challenging to solve, especially when the number of users becomes large. Sparked by the correlation features of the users' channels in mm-wave-NOMA systems, we develop a K-means-based machine learning algorithm for user clustering. Moreover, for a practical dynamic scenario where the new users keep arriving in a continuous fashion, we propose a K-means-based online user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mm-wave-NOMA system, we derive the optimal power allocation policy in a closed form by exploiting the successive decoding feature. Simulation results reveal that: 1) the proposed machine learning framework enhances the performance of mm-wave-NOMA systems compared to the conventional user clustering algorithms and 2) the proposed K-means-based online user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.

The joint design of beamforming and artificial noise vectors is proposed for the full-duplex simultaneous wireless information and power transferring (FD-SWIPT) systems when multiple eavesdroppers can wiretap information from FD base station (FD-BST) and FD user equipment (FD-UE). To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a sum-information-transmission-rate-maximization (SITRM) problem under information-leakage and energy constraints. Besides, we consider the fairness issue between uplink and downlink information-transmission rates by formulating a fairness-aware-SITRM (FA-SITRM) problem. Since the eavesdroppers can receive the information of FD-BST and FD-UE, the information-leakage region becomes non-convex and challenging to handle. Hence, we propose efficient algorithms to solve the SITRM and FA-SITRM problems optimally via semidefinite relaxation (SDR) and a 1-D search. In each search iteration, our proposed algorithms can recover the rank-one constraints when the application of SDR cannot obtain the optimal solutions. Moreover, we analyze the computational complexities and propose two suboptimal solutions to the formulated problems. For the SITRM problem, our numerical results show that the performance achieved by one of two suboptimal algorithms is close to the performance of optimal algorithm with increasing the maximum transmission power of FD-BST.

Conventional identification algorithms of space- frequency block codes (SFBC) only utilize the space- domain redundancy between any two receive antennas. In this paper, a novel two-dimensional space-frequency domain redundancy based SFBC identification algorithm for frequency selective fading is proposed in which the detection probability varies with the number of subcarriers. In particular, space-domain redundancy is utilized to construct the cross-correlation function of the estimator while frequency-domain redundancy is incorporated in the hypothesis test statistic. Simulation results verify the viability of the proposed algorithm and its superior performance for short observation periods with comparable computational complexity to the conventional algorithms.

Narrowband power line communications (NB-PLC) and unlicensed wireless communications are the two leading communications technologies for the emerging two-way Vehicle-to-Grid (V2G) communications applications. In this paper, we exploit the media diversity provided by the availability of both the NB-PLC and wireless links to enhance the robustness against the interference and noise encountered on both links. In particular, we propose two efficient media diversity selection schemes to exploit the decoupled nature of the two communication links to reduce the bit error rate (BER) and increase the reliability of the V2G communication system. In addition, we derive closed-form expressions for the BER of our proposed media diversity selection schemes. Furthermore, we present simulation results that quantify the performance gains achieved by our proposed media diversity selection schemes compared to a single link (i.e. wireless-only or PLC-only) performance.

In this paper, the problems of relay selection and distributed beamforming are investigated for bi-directional dual-hop amplify-and-forward frequency-division duplex cooperative wireless networks. When using individual per-relay maximum transmission power constraint, it has been proven that the relay selection and beamforming optimization problem becomes NP hard and requires exhaustive search to find the optimal solution. Therefore, we propose a computationally affordable suboptimal multiple relay selection and beamforming optimization scheme based on the ℓ1norm squared relaxation. The proposed scheme performs the selection for the two transmission directions, simultaneously, while aiming at maximizing the aggregated SNR of the two communicating nodes. Furthermore, by exploiting the previous solutions to accelerate the algorithm's convergence, our proposed algorithm converges to a suboptimal solution compared to the exhaustive search technique with much less complexity.

In this paper, we propose a novel approach to recover off-grid narrow-band-interference (NBI) in orthogonal-frequency-division-multiplexing (OFDM) systems using a compressive-sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain (FD). However, frequency-grid mismatch destroys the sparsity of NBI in the FD. Therefore, the received off-grid NBI is characterized by a nonlinear model which is parametrized by two vectors; the first vector represents the frequency-grid-mismatch and the second vector represents the FD sparse NBI. A CS- based particle-swarm-optimization (CS-PSO) evolutionary algorithm is proposed based on a weighted sum of l_2 andl_0 norms fitness function to jointly recover the support of the FD sparse vector and the frequenc ygrid- mismatch vector. Simulation results demonstrate the merits of the proposed approach.

Next-generation wireless networks target dense deployment scenarios where many heterogeneous devices, from high-end laptops to low-power Internet of Things (IoT) devices and wearables, must coexist and operate reliably. With increased deployment of IoT devices and wearables, increasing the energy efficiency without increasing complexity#x002F;cost is highly desirable in future dense wireless networks. In current wireless local area networks (WLAN), implementing multi-input multi-output (MIMO) techniques which utilizes multiple RF chains has become the norm, however, for IoT and wearable-type devices this is not efficient due to size and cost constraints. This paper studies the application of the Spatial Modulation (SM) waveform to the WLAN system (commercially named Wi-Fi). Specifically, the SM concept is applied to the MIMO orthogonal frequency division multiplexing (OFDM) transceiver architecture of the 802.11 WLAN standards calling it spatially-modulated OFDM (SM-OFDM) WLAN. Thus, using a small number of RF chains, and ideally a single RF chain, is highly desirable for future low-cost devices. The proposed SM-OFDM scheme helps the Access Point (AP) to efficiently communicate with this diverse set of devices while addressing challenging design trade-offs between energy efficiency, implementation complexity, and overall network spectral efficiency.

In this paper, we propose a general framework that transforms the problems of designing sparse finite-impulse-response linear equalizers and nonlinear decision-feedback equalizers, for multiple antenna systems, into the problem of sparsest approximation of a vector in different dictionaries. In addition, we investigate several choices of the sparsifying dictionaries under this framework. Furthermore, the worst case coherences of these dictionaries, which determine their sparsifying effectiveness, are analytically and/or numerically evaluated. Moreover, we show how to reduce the computational complexity of the designed sparse equalizer filters by exploiting the asymptotic equivalence of Toeplitz and circulant matrices. Finally, the superiority of our proposed framework over conventional methods is demonstrated through numerical experiments.

We investigate the physical layer security of synchronous multiple access transmissions using the Alamouti space-time block code in fading channels where multiple users communicate with a single intended receiver in the presence of an eavesdropper. We propose an artificial-noise-aided technique to secure the transmissions by having the Alamouti users collaborate with each other, without exchanging information, to degrade the eavesdropper's channel. Unlike previous work, which assumes that the transmitters have complete knowledge of the legitimate as well as the eavesdropper's channels, our proposed technique requires no communications between the users, minimal knowledge of the legitimate channel, and no channel knowledge regarding the eavesdropper.

The emerging millimeter-wave (mm-wave) MIMO systems are subject to strong radio frequency (RF) distortions and their compensation is crucial to realize such systems. In the existing literature, several estimation and compensation schemes have been proposed for RF distortions, such as carrier frequency offset, phase noise, and in-phase and quadrature amplitude and phase imbalance (IQI). However, in-phase and quadrature timing mismatch (IQTM) is largely ignored. This paper investigates the effect of the IQTM on mm-wave system performance and reveals that IQTM causes a specific image rejection ratio characteristic, which is substantially different from the regular frequency-dependent IQI and it substantially prolongs the effective channel length. If not compensated, the IQTM can degrade system performance significantly. As a solution to this IQTM problem, this paper proposes novel pilot designs for transmit and receive IQTM estimation, and develops corresponding estimators, transmission protocol, and compensation schemes. MIMO averaging is also proposed, which substantially enhances the IQTM estimation performance. Simulation results show that our proposed pilot designs and estimators offer an efficient solution to the IQTM problem.

Narrowband powerline communications (NB-PLC) and unlicensed wireless communications are two leading communications technologies for the emerging smart grid applications. The channel and noise statistics experienced by powerline and wireless transmissions are independent and of a non-identical nature. In this paper, we exploit the diversity provided by the simultaneous transmission of the same information signal over powerline and wireless links to enhance the overall system reliability. In particular, we propose efficient techniques to combine the received signals of the NB-PLC and wireless links for both coherent and differential modulation schemes while considering the impulsive nature of the noise on both links. In addition, we derive closed-form expressions for the average bit-error-rate of the proposed combining techniques. Furthermore, we present simulation results that quantify the performance gains achieved by our proposed receive diversity combining techniques compared with conventional combining techniques.

This paper proposes a cross-layer design to enhance the security of a set of buffered legitimate source nodes wishing to communicate with a common destination node using a time-division multiple-access scheme with probabilistic time slot assignment. The users' assignment probabilities to the time slots are optimized to satisfy a certain quality-of-service (QoS) requirement for each of the legitimate source nodes. To further improve the system security, we propose beamforming-based cooperative jamming schemes subject to the availability of the channel state information (CSI) at the legitimate nodes. We assume that if a source node is not selected for data transmission, it is a cooperative jamming node. We impose an average transmit power constraint (averaged across time slots) on each source node. Hence, the source nodes should efficiently distribute their average transmit powers throughout the network operation between data and artificial noise transmissions to satisfy the QoS requirements. We investigate the two cases where a global CSI is assumed at the legitimate nodes and where there is no eavesdropper's CSI. The case where there is no CSI at the jamming nodes is also investigated and a new scheme is proposed. We derive closed-form expressions for the instantaneous secrecy rate for each scheme as well as the secrecy outage probability. Moreover, we derive the secrecy stable-throughput and delay-requirement regions of the network. Our proposed jamming schemes achieve significant increases in the secure throughput over existing schemes from the literature and over the no-jamming scheme.

In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.

We investigate the physical layer security of synchronous multiple access transmissions using the Alamouti space-time block code in fading channels where multiple users communicate with a single intended receiver in the presence of an eavesdropper. We propose an artificial-noise-aided technique to secure the transmissions by having the Alamouti users collaborate with each other, without exchanging information, to degrade the eavesdropper's channel. Unlike previous work, which assumes that the transmitters have complete knowledge of the legitimate as well as the eavesdropper's channels, our proposed technique requires no communications between the users, minimal knowledge of the legitimate channel, and no channel knowledge regarding the eavesdropper.

Differential space time block coding (STBC) achieves full spatial diversity and avoids channel estimation overhead. Over highly frequency-selective channels, STBC is integrated with orthogonal frequency division multiplexing (OFDM) to achieve high performance. However, low-cost implementation of differential STBC- OFDM using direct-conversion transceivers is sensitive to In-phase/Quadrature-phase imbalance (IQI). In this paper, we quantify the performance impact of IQI at the receiver front-end on differential STBC-OFDM systems and propose a compensation algorithm to mitigate its effect. The proposed receiver IQI compensation works in an adaptive decision-directed manner without using known pilots or training sequences, which reduces the rate loss due to training overhead. Our numerical results show that our proposed compensation algorithm can effectively mitigate receive IQI in differential STBC-OFDM.

In this paper, we propose a cyclostationary noise model for narrowband power line communication (NB-PLC) based on frequency-shift (FRESH) filtering. The FRESH filters are designed to shape an input white noise spectrum to a cyclic spectrum extracted from experimental noise measurements. The noise modeling problem is considered as a system identification problem that minimizes the time-averaged mean square error (TA-MSE) between a reference measured noise and the model generated noise. We propose the normalized mean square error (NMSE) in the cyclic auto-correlation between the measured and the generated noise as a performance measure. The noise waveform generated by the proposed model shows a good agreement with that of measured noise in terms of the cyclic auto-correlation NMSE. In addition, the numerical results show a performance enhancement, in terms of the cyclic auto-correlation NMSE, over the time-region-based NB-PLC noise model presented in the literature, at the same model complexity. Moreover, the proposed model provides a scalable performance-complexity tradeoff.

Visible light communications (VLC) technology has recently emerged as a complementary technology to the indoor radio frequency (RF) networks. While the backhauling of the indoor RF networks has been supported by fiber optics, the backhauling of the VLC network is still an open research problem. Power line communications (PLC) has been considered as a possible solution for the VLC networks due to its ubiquity. However, given the noisy behaviour of the channels, the use of PLC backhauling requires further study to investigate the maximum rate which can be supported for VLC networks. In this paper, we continue our work on a single-user hybrid PLC/VLC/RF system [1] to study the effect of PLC backhauling in multi-user hybrid VLC/RF networks. To this end, we adopt an orthogonal frequency division multiplexing (OFDM)- based PLC backhaul system where we find the optimal power and subcarrier allocation algorithm that maximizes the multi-user rate utility function for the cascaded PLC and VLC links in parallel with the RF network. Moreover, we study numerically the impact of various parameters on the system performance including the PLC transmission power, the RF and VLC maximum allowable rates, and the numbers of mobile terminals and access points.

This paper investigates artificial noise injection into the temporal and spatial dimensions of a legitimate wireless communication system to secure its transmissions from potential eavesdropping. We consider a multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) system in the presence of a single-antenna passive eavesdropper and derive both the secrecy rate and average secrecy rate of the legitimate system. It is assumed that the legitimate transmitter knows the full channel information of the legitimate transceivers but does not know the instantaneous channel state information of the passive eavesdropper. Closed-form expressions for the secrecy rate and average secrecy rate are derived for the asymptotic case with a large number of transmit antennas. We also investigate 1) the power allocation between the data and the AN; 2) the power allocation between the spatial and the temporal AN. Computer simulations are carried out to evaluate the performance of our proposed artificial noise scheme.

A major performance and complexity limitation in broadband communications is the long channel delay spread which results in a highly-frequency-selective channel frequency response. Channel shortening equalizers (CSEs) are used to ensure that the cascade of a long channel impulse response (CIR) and the CSE is approximately equivalent to a target impulse response (TIR) with much shorter delay spread. In this paper, we propose a general framework that transforms the problems of design of sparse CSE and TIR finite impulse response (FIR) filters into the problem of sparsest-approximation of a vector in different dictionaries. In addition, we compare several choices of sparsifying dictionaries under this framework. Furthermore, the worst-case coherence of these dictionaries, which determines their sparsifying effectiveness, are analytically and/or numerically evaluated. Finally, the usefulness of the proposed framework for the design of sparse CSE and TIR filters is validated through numerical experiments.

In this paper, a novel approach is proposed to jointly recover the transmitted signal and mitigate narrow-band interference (NBI) in OFDM systems using a compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as sparse atoms and then to solve a joint compressive sensing (JCS) problem. The recovery problem is formulated as a weighted optimization problem of two atomic norms and then solved using convex programming. The solution aims to recover the transmitted signal and NBI jointly. The off-grid problem of NBI signal is avoided using the atomic norm. The bit error rate (BER) performance of our proposed JCS recovery approach outperforms the BER performance of the conventional CS-based approach as demonstrated by simulation results.

Underlay cognitive networks should follow strict interference thresholds to operate in parallel with primary networks. This constraint limits their transmission power and eventually the coverage area. Therefore, in this paper, we first design a new approach for asynchronous narrow-band interference (NBI) estimation and mitigation in orthogonal frequency-division multiplexing cognitive radio networks that does not require prior knowledge of the NBI characteristics. Our proposed approach allows the primary user to exploit the sparsity of the secondary users' interference signal to recover it and cancel it based on sparse signal recovery theory. We also propose two subcarrier selection schemes that allow the primary user to further reduce the effect of the secondary users' interference based on sparse signal recovery algorithms. We show that although the primary and secondary transmissions are performed at the same time, the performance of our proposed techniques approach the interference- free limit over practical ranges of NBI power levels.

In this paper, the performance of an orthogonal frequency division multiplexing overlay cognitive radio network with subcarrier selection schemes is investigated. We propose three subcarrier selection techniques that reduce the level of interference at the primary base station based on collected channel state information from the different network nodes. Approximated outage probability expressions are also derived and verified by simulations for the different studied techniques. In addition, we propose and investigate a new approach for asynchronous narrowband interference (NBI) estimation and mitigation in cognitive radio networks. The proposed approach does not require prior knowledge of the NBI characteristics and allows the primary user to exploit the sparsity of the secondary users interference to recover it based on sparse signal recovery theory and approach the interference-free limit over practical ranges of NBI power levels.

In this paper, narrow-band interference (NBI) mitigation is addressed for two-way (TW) relaying amplify and forward (AF) orthogonal frequency division multiplexing (OFDM) systems. Based on the channel gains between the interferer, relay and the terminal nodes two copies of the NBI signal are received at each terminal node in addition to the desired signal. Hence, NBI can degrade the performance of TW-AF-OFDM relaying systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency domain, hence, the compressive sensing (CS) framework can be used to recover NBI and cancel it before detecting the transmitted signal. However, frequency-grid-mismatch destroys the sparsity of the received NBI signal at the terminal nodes. Hence, we propose a block-structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI. The block orthogonal matching pursuit (B-OMP) algorithm is proposed to solve the formulated optimization problem because of its reduced computational complexity. Simulation results demonstrate the merits of the proposed approach.

In this paper, three novel techniques are proposed and investigated for multiple relay selection in dual hop OFDM networks. These techniques are based on the exploitation of sparse signal recovery theory and on carefully-designed groupings of the subcarriers depending on the channel quality. In particular, the proposed techniques use the Orthogonal Matching Pursuit algorithm which enables them to outperform existing techniques in terms of both outage probability and computation complexity. Furthermore, a detailed performance-complexity tradeoff investigation is presented for the different studied techniques and verified by Monte Carlo simulations.

We consider a multiple-input multiple-output (MIMO) channel with inter-symbol interference (ISI) where signal detection is highly complex due to the large signal state-space dimensionality. A common strategy is to apply a front-end filter (FEF) to eliminate the ISI dimension (i.e. full ISI equalization). This FEF is then followed by a frequency non-selective MIMO detector. However, another, much less researched, strategy is to apply an FEF that eliminates the MIMO dimension and results in a set of parallel ISI channels followed by a bank of parallel single-input single-output (SISO) detectors. Which of the two strategies is better in a Shannon capacity sense? In this paper, we show that the answer to this question depends on system parameters such as SNR, number of antennas, ISI duration, and spatial correlation properties.

In this paper, narrow-band interference (NBI) mitigation is addressed in amplify-and-forward orthogonal-frequency-division-multiplexing (AF-OFDM) cooperative communication systems. Based on the channel gains between the interferer, destination and the relay nodes, three copies of the NBI are received at the destination node in addition to the desired signal. Hence, NBI degrades the performance of AF-OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain, hence, compressive sensing (CS) framework can be used to estimate and cancel the NBI before detecting the transmitted signal. However, frequency-grid-mismatch destroys the sparsity of NBI in the frequency domain at the destination terminal. A structured-dictionary-mismatch formulation is proposed to approximate the received NBI vector by two sparse vectors. An £2,1 norm minimization problem is solved to recover the sparse vectors. The recovered NBI is then canceled from the received signal before detection. Simulation results demonstrate the merits of the proposed approach.

Presents the introductory editorial for this issue of the publication.

Prior results on exact error rates and outage probabilities of diversity receptions over arbitrarily correlated fading channels involve intractable nested integrals, which makes the performance analyses challenging to carry out. In this paper, we derive asymptotic expressions and asymptotically tight bounds for error rates and outage probabilities of diversity receptions (including maximal-ratio combining, selection combining, and equal-gain combining) over arbitrarily correlated Beckmann fading channels. These new asymptotic performance expressions reveal insights into the diversity systems over arbitrarily correlated Beckmann fading channels, and the accuracy of the asymptotic performance expressions is explicitly quantified by the performance bounds. Using these analytical tools, one can assess the performance of the diversity reception systems over arbitrarily correlated Beckmann fading channels without resorting to time-consuming Monte Carlo simulation or numerical multifold integration.

In this paper, a novel dynamic power allocation scheme is proposed to downlink and uplink non-orthogonal multiple access (NOMA) scenarios with two users for more flexibly meeting various quality of service requirements. The exact expressions for the outage probability and the average rate achieved by the proposed scheme, as well as their high signal-to-noise ratio approximations, are established. Compared with the existing works, such as NOMA with fixed power allocation and cognitive radio inspired NOMA, the proposed scheme can: 1) strictly guarantee a performance gain over conventional orthogonal multiple access; and 2) offer more flexibility to realize different tradeoffs between the user fairness and system throughput. Monte Carlo simulation results are provided to demonstrate the accuracy of the developed analytical results and the performance gain of the proposed power allocation scheme.

The increased demands for indoor wireless data services with the limited available radio frequency (RF) spectrum are motivating researchers to investigate alternative technologies to augment the existing RF networks. Utilizing the visible light spectrum by exploiting visible light communication (VLC) has shown strong potential to augment RF networks in indoor scenarios. The ubiquity of the power-line network makes it an attractive choice as a communication medium between the data sources and the VLC transmitters through power line communication (PLC). Hence, integrating VLC and PLC systems can be a strong complementary hybrid wireline/wireless technology for indoor data networks. In this paper, we investigate the power allocation problem for a communication scenario in which data is transferred through a cascaded PLC/VLC channel in parallel to an RF wireless channel. We develop an algorithm for allocating the transmission powers among the communication nodes to maximize the achievable rate. Our results show that the proposed system provides appreciable rate gains compared to conventional RF communication systems for the same amount of total transmission power.

Exact error rate expressions for equal-gain combining over arbitrarily correlated Rician channels involve intractable integrals. Thus, asymptotic error rate expression was derived to provide accurate approximation in large signal-to-noise ratio region. However, the existing asymptotic analysis cannot evaluate the accuracy of the asymptotic error rate expression for equal-gain combining. In this work, we derive closed-form and asymptotically tight upper and lower bounds for the error rate of equal-gain combining over Rician fading channels with arbitrary correlation. These bounds can be used to evaluate the accuracy of the asymptotic error rate expression for equal-gain combining at high signal-to-noise ratio without using the time-consuming Monte Carlo simulation.

This paper proposes efficient multiple-access schemes for large wireless networks based on the transmitters' buffer state information and their transceivers' duplex transmission capability. First, we investigate the case of half-duplex nodes where a node can either transmit or receive in a given time instant. In this case, for a given frame, the transmitters send their buffer states to the destination which assigns the available time duration in the frame for data transmission among the transmitters based on their buffer state information. The network is said to be naturally sparse if the number of nonempty-queue transmitters in a given frame is much smaller than the number of users, which is the case when the arrival rates to the queues are very small and the number of users is large. If the network is not naturally sparse, we design the user requests to be sparse such that only few requests are sent to the destination. We refer to the detected nonempty-queue transmitters in a given frame as frame owners. Our design goal is to minimize the nodes' total transmit power in a given frame. In the case of unslotted-time data transmission, the optimization problem is shown to be a convex optimization program. We propose an approximate formulation to simplify the problem and obtain a closed-form expression for the assigned time durations to the nodes. The solution of the approximate optimization problem demonstrates that the time duration assigned to a node in the set of frame owners is the ratio of the square-root of the buffer occupancy of that node to the sum of the square-roots of each occupancy of all the frame owners. We then investigate the slotted-time data transmission scenario, where the time durations assigned for data transmission are slotted. In addition, we show that the full-duplex capability of a node increases the data transmission portion of the frame and enables a distributed implementation of the proposed schemes. Our numerical results demonstrate that the proposed schemes achieve higher average bits per unit power than the fixed-assignment scheme where each node is assigned a predetermined fraction of the frame duration.

A secure space-time block coding (STBC) scheme for 2 and 4 transmit antennas is proposed in the presence of a passive eavesdropper. The bit error probability is analyzed for coherent detection as well as with differential detection. The analytical expressions are validated through simulations and demonstrate that our proposed secure STBC guarantees full diversity to an intended recipient while reducing the diversity order of an eavesdropper to zero even when she has perfect channel state information.

We study the problem of multi-stream joint maximum sum-rate precoder and minimum mean-squared error equalizer design for interference alignment in the multi-input multi-output interference channel. Joint precoder and equalizer optimization requires alternation between the forward and reverse links and assumes perfect synchronization at each network node between the transmitters and receivers, resulting in extensive overhead and spectral efficiency loss. To overcome this serious drawback, we propose a new design approach based on weighted-sum-rate maximization assuming a virtual equalizer type at the transmitter to limit the optimization process to the transmitter side. Furthermore, we examine the achievable weighted-sum-rate when the virtual equalizer type is either matched or mismatched to the actual equalizer type used at the receivers. In addition, we quantify the performance loss due to mismatched equalizer types and demonstrate the robustness of our proposed sum-rate weighting strategy to such mismatches. Finally, we derive asymptotic performance expressions and verify their accuracy numerically, even for a moderate number of users.

We analyze the outage probability of dual-hop full-duplex decode-and-forward relaying for an orthogonal frequency division multiplexing system in the presence of I/Q imbalance. We derive accurate analytical approximations which quantify the outage probability's functional dependence on the I/Q imbalance level and the residual loopback self-interference average power level. In addition, we derive the condition at which direct transmission outperforms full-duplex decode-and-forward relay-assisted transmission in the presence of I/Q imbalance. Furthermore, we propose an opportunistic relaying approach and demonstrate its robustness against the detrimental effects of I/Q imbalance and residual loopback self-interference. Our numerical results confirm the accuracy of our analysis.

Exploiting multiple physical layers for communications has gained increasing interest recently to improve reliability and/or coverage range. Powerline and unlicensed wireless communication networks are attractive candidates to realize this objective because of their ubiquity. However, their performance can be severely degraded by impulsive noise (IN) and narrowband interference (NBI), respectively. In this paper, we exploit the inherent sparse structures of NBI and IN in the frequency and time domains, respectively, to propose an efficient joint estimation and mitigation scheme based on compressive sensing (CS) principles. Moreover, we investigate the metric of maximum expected coherence of our scheme for realistic powerline communication (PLC) and wireless channels, which provides some insight into its performance. Finally, our numerical experiments demonstrate the superiority of jointly processing the wireless and PLC channel outputs for CS-based NBI and IN mitigation over separate processing of individual channel outputs.

We propose a novel protocol for secondary users based on compressive sensing principles. The secondary user assigned to one of the primary frequency bands is aided by a set of nearby secondary users, which we refer to as secondary relays. We assume that each secondary relay participates in relaying the secondary packets with certain probability. In a given time slot, each secondary relay indicates its ability of decoding the secondary packet and being a relay for the secondary transmission. Our proposed protocol exploits the sparsity of the participating relays set and is efficient as it allows dynamic relay assignment. When the primary user is inactive, the relays send their states to the secondary source which, in turn, transmits its own packet if the received number of decoding relays satisfies a predefined threshold. When the primary user is active, the relays perform cooperative beamforming to achieve a cooperative diversity gain for the secondary source while completely eliminating the interference to the primary destination. We study the performance of the system from a cross-layer point of view.

Prior work on exact error rate of diversity receptions over arbitrarily correlated Rician channels involves intractable integrals. Although asymptotic error rates provide closed-form approximations which are accurate at high signal-to-noise ratio, it is still unknown at what signal-to-noise ratio the approximation becomes accurate. In this paper, asymptotically tight closed-form upper and lower bounds are derived for maximal-ratio combining and selection combining over arbitrarily correlated Rician channels. Using these bounds, we can show at what signal-to-noise ratio that the asymptotic error rates are accurate without resorting to time-consuming Monte Carlo simulation.

In this paper, we study the problem of per-stream maximum sum-rate joint precoder and minimum mean-squared error equalizer design for the multi-input multi-output interference channel. We consider the general case of more than three users with more than one stream per user. We propose a generalized iterative algorithm which directly maximizes the sum-rate without assuming the signal-to-noise ratio to be infinite. To reduce complexity, which can become prohibitive for large network size, we examine the performance-complexity tradeoffs involved in a sparse equalizer design. Joint precoder and equalizer optimization requires alternation between the forward and reverse links and assumes perfect synchronization between the transmitters and receivers at each network node, resulting in extensive overhead and spectral efficiency loss. To overcome this serious drawback, we propose a new design approach based on weighted-sum-rate maximization assuming a virtual equalizer type at the transmitter to limit the optimization process to the transmitter side. In addition, we quantify the sum-rate loss due to mismatched equalizer types and demonstrate the robustness of our proposed sum-rate weighting strategy to such mismatches with perfect or imperfect channel knowledge. Finally, we derive asymptotic performance expressions and verify their accuracy numerically even for a moderate number of users.

We consider a cognitive setting composed of multiple primary frequency bands each of which consists of a limited-battery energy-harvesting primary user and a secondary user. The primary user is equipped with a limited-capacity energy queue to maintain the energy harvested from the environment. We propose a novel cooperative protocol for the secondary users. The secondary user assigned to one of the primary frequency bands is aided by a set of nearby secondary users, which we refer to as secondary relays. We assume that each secondary relay participates in relaying the secondary packets with a certain probability. The proposed protocol exploits the sparsity of the participating-relays set and is efficient as it allows dynamic relay assignment. When the secondary user's direct link is in outage, the received secondary packets at the relays are stored in a relaying queue for future retransmissions. The relays perform cooperative beamforming to achieve a cooperative diversity gain for the secondary source. We study two types of quality-of-service requirements for the primary user. We analyze the impact of the primary user energy queue arrival rate and capacity on the secondary user throughput. In addition, we investigate the impact of the relaying queue capacity and the number of relays participating in spectrum sensing on the secondary user throughput.

We analyze the EVM performance of OFDM-based relay-assisted amplify-and-forward systems in the presence of I/Q imbalance. We show analytically that the MRC detector suffers from an error floor due to mutual interference between image subcarriers. We prove analytically that a simple zero-forcing two-tap per subcarrier equalizer eliminates this error floor. Simulation results demonstrate its significant performance gain over MRC detection for different I/Q imbalance levels, SNR levels, and relay positions.

Narrowband power line communications (NB-PLC) and unlicensed wireless communications are considered as the two leading two-way communications technologies for the emerging Smart Grid applications. In this paper, we propose exploiting the diversity provided by combining the received signals from the NB-PLC and wireless links, where both carry the same information, to provide robustness against the interference encountered on both links. First, we analyze the performance of each link separately in presence of impulsive noise and interference. Then, we study the performance when combining the output signals from both links and compare it to the performance of each link.

We investigate the spectrum sensing problem in orthogonal frequency-division multiplexing-based cognitive radio networks under I/Q imbalance. We start by deriving the likelihood ratio test in the presence of I/Q imbalance at the analog front-ends of both the primary and secondary users. In addition, we derive closed-form expressions for the probabilities of detection and false alarm and the receiver operating characteristics and examine their dependence on both transmit and receive I/Q imbalance levels. Furthermore, we compare the performance of the likelihood ratio test with that of the energy detector and demonstrate the superiority of the former over the latter. Next, we generalize our analysis to the blind case where we derive simple closed-form expressions for the generalized likelihood ratio test and its false alarm probability as a function of the received signal only, i.e. without requiring any knowledge of the primary-to-secondary channel response, noise statistics, or I/Q imbalance parameters. Our results demonstrate that the correlation properties of the primary user's signal induced by transmit I/Q imbalance are signal features that can be exploited in a blind fashion at the secondary user to enhance the detection probability significantly compared to the conventional energy detector.

We study the outage performance of orthogonal frequency-division multiplexing dual-hop opportunistic amplify-and-forward relaying in the presence of I/Q imbalance (IQI) in all nodes. We derive a closed-form expression for the end-to-end outage probability for the general case where each node suffers from a different IQI level and assuming that the distance between source and relay is not the same for all relays. Furthermore, we consider the more general case where there is a direct link between the source and the destination and we derive a closed-form expression for the end-to-end outage probability for both maximum-ratio and selection combining at the destination. To gain more insights, we analyze a special case where all relays lie on the perpendicular line midway between source and destination and all nodes experience the same IQI level. Our simulations show excellent match to the analytical results and both demonstrate that uncompensated IQI can be detrimental but it can also be effectively mitigated using a few opportunistic relays. In summary, the main contributions of this paper are analyzing the effect of IQI on the outage probability of an opportunistic relaying system and determining the number of relays needed to effectively mitigate uncompensated IQI.

We study the problem of per-stream joint maximum sum-rate (MSR) precoder and minimum mean-squared error (MMSE) equalizer design for interference alignment in the multi-input multi-output interference channel. We consider the general case of more than three users with more than one stream per user. We propose a new generalized iterative algorithm which directly maximizes the average overall sum-rate without assuming the signal-to-noise ratio to be infinite. The receivers' implementation complexity increases proportional to the square of the number of equalizer taps which becomes prohibitive as the network size increases. To address this issue, we examine the performance-complexity tradeoffs involved in a sparse equalizer design. Our numerical results demonstrate that, for the full-complexity MMSE equalizer design, our proposed algorithm achieves higher overall sum-rate compared to previously proposed interference alignment algorithms. In addition, we reduce the MMSE linear equalizer complexity by 30% while limiting the sum-rate loss to about 10%, at most, compared to the full-complexity design.

We study the outage performance of orthogonal frequency-division multiplexing dual-hop opportunistic amplify-and-forward relaying in the presence of I/Q imbalance in all of the nodes' transceivers. We derive a closed-form expression for the end-to-end outage probability for the general case, where each node suffers from a different I/Q imbalance level and under the assumption that the distance between source and relay is not the same for all relays. Our simulation results show excellent match to our analysis and both demonstrate that uncompensated I/Q imbalance can be detrimental but it can also be effectively mitigated using a few relays. This result has a significant impact on relay deployment in future wireless communication networks.

In this paper, we investigate cooperative Single-Carrier Frequency-Domain Equalization (SC-FDE) for two-way relay networks, where multiple users each equipped with multiple antennas exchange their information through a multi-antenna relay node in a bi-directional manner. Under network total power constraint, we present optimal relay beamforming for the multiuser two-way relay system, where the relay transceiver processor is designed based on the minimum mean-square-error (MMSE) criterion. We drive a closed-form expression for the signal-to-interference noise ratio (SEVR) at each of the user terminals, and further present a joint user-relay antenna selection algorithm by applying the estimation of distribution algorithm (EDA). The proposed EDA has a low computational complexity, and its effectiveness is verified through simulation results.

The direct-conversion architecture used for Orthogonal Frequency Division Multiplexing (OFDM) systems suffers from intercarrier interference (ICI) between image subcarriers in each OFDM symbol due to I/Q imbalance between the inphase (I) and quadrature (Q) branches at the transmit and receive sides. One of the widely-used metrics to evaluate the performance degradation due to I/Q imbalance is the signal to interference plus noise ratio (SINR). The instantaneous subcarrier SINR can be represented as a ratio conditioned on a particular channel realization and the exact SINR is evaluated by averaging over the channel realizations. In the literature, it is common to approximate the SINR by taking the average of the numerator and denominator separately although the two are not independent. In this paper, we calculate the exact SINR under the assumption that each pair of interfering subcarriers experiences uncorrelated channel realizations. We show that the SINR grows as the logarithm of the input SNR when the receiver has I/Q imbalance. On the other hand, under transmit-only I/Q imbalance, there is an SINR ceiling for large input SNR. Therefore, transmitter side I/Q imbalance is more harmful, in terms of average SINR, than receiver-only I/Q imbalance. We also show that, the approximate SINR expression, commonly used in the literature, is not accurate for large input SNR values, except for the case of transmit-only I/Q imbalance.

We analyze the outage performance of half-duplex amplify-and-forward relaying in an OFDM system with MRC detection in the presence of I/Q imbalance and compare it with that of the direct transmission mode. Both analytical and numerical results demonstrate that the direct mode can outperform the amplify-and-forward mode even under moderate levels of uncompensated I/Q imbalance. The cross-over I/Q imbalance levels are determined analytically to be inversely proportional to the cube of the signal constellation size. In addition, we present a low-complexity receiver-based digital baseband I/Q imbalance compensation scheme for the amplify-and-forward mode and analyze its EVM performance. Furthermore, we derive accurate analytical approximations for the EVM performance as a function of relay location and I/Q imbalance level with and without compensation.

In this paper, we start by investigating the impact of joint transmit-receive I/Q imbalance on the performance of direct-conversion beamforming OFDM transceivers. We derive a new analytical expression for the average subcarrier SINR of the I/Q imbalance-ignorant beamformer in terms of the I/Q imbalance level at both the transmit and receive sides, the size of the beamforming array, and the input SNR level. This expression motivates the need for I/Q imbalance compensation and provides valuable design insights when examining several special and limiting cases. Next, we derive the throughput-maximizing multiple beamforming transmit/receive coefficients under a general system model with an asymmetric frequency-dependent (FD) joint transmit-receive I/Q imbalance. In addition, we propose a low-complexity pilot-aided scheme for the estimation of the channel and I/Q imbalance parameters. Our simulation results demonstrate that the proposed generalized multiple beamforming scheme is highly effective in mitigating I/Q imbalance effects at practical complexity levels.

We consider the spectrum sensing problem in orthogonal frequency-division multiplexing-based cognitive radio networks under I/Q imbalance. We derive the likelihood ratio test in the presence of colored noise and I/Q imbalance at the analog front-ends of both of the primary and secondary users. Furthermore, we derive the probabilities of detection and false alarm and develop the receiver operating characteristics. We also examine the impacts of both transmit and receive I/Q imbalance on the performance of the likelihood ratio test. We compare the performance of our likelihood ratio test with that of the energy detector through extensive simulations and show the superiority of the former over a wide range of signal-to-noise ratios.

We analyze the outage performance of relay-assisted amplify-and-forward systems under uncompensated I/Q imbalance effects and derive accurate analytical approximations for both cases. In addition, we compare the outage probability for amplify-and-forward systems with that of the direct transmission mode. Both analytical and numerical results demonstrate that the direct mode can outperform the amplify-and-forward mode even under moderate levels of uncompensated I/Q imbalance. The cross-over I/Q imbalance levels are determined analytically to be inversely proportional to the cube of the signal constellation size. Furthermore, we propose a low-complexity receiver-based digital baseband I/Q imbalance compensation scheme for the amplify-and-forward mode. Numerical results show its performance superiority to the conventional maximal-ratio combining scheme even under imperfect channel state information (CSI).

In this paper, we present a novel approach for the design of rate-maximizing channel shortening detectors with soft feedback side information for frequency-selective channels. The detector is a soft-input soft-output detector and constitutes one of the components of an iterative receiver. The design optimization is performed from an information-theoretic perspective where we maximize the achievable rate during each step of the iterative process. Our proposed detector consists of a front-end filter whose coefficients are given in closed form in addition to a convex optimization procedure which provides the branch labels of the trellis and the feedback filter coefficients. The detector can be implemented as a BCJR-type algorithm operating on a trellis where the number of states is a user-defined parameter.

The performance of digital subscriber line (DSL) systems, such as ADSL and VDSL is limited by crosstalk. Suppression of in-domain far-end self crosstalk using vectoring technology enables very high bidirectional data rates over twisted-pairs of copper wires. However, the performance of vectored DSL systems is severely degraded in the presence of alien or out-of-domain crosstalk that arises from sources that lie outside the vectored DSL system and share the same cable binder. In this paper, we propose a practical, non-iterative, high performance algorithm for alien crosstalk mitigation. Simulation results and complexity analysis corresponding to a vectored VDSL2 system in presence of alien crosstalk are presented to illustrate the significant performance gains of the proposed algorithm and its low implementation complexity.

We propose an iterative joint detection and phase noise compensation scheme for single-carrier frequency-division-multiple-access systems. Our proposed scheme does not require any pilots in tracking the phase noise and assumes no knowledge about the phase noise model to avoid model mismatch problems. Simulation results show the effectiveness of our proposed scheme in compensating phase noise effects with only few iterations.

In this paper, we investigate the effects of asymmetric frequency-dependent joint transmit-receive I/Q imbalance on the performance of beamforming MIMO-OFDM systems. We derive the throughput-maximizing transmit/receive beamforming coefficients taking into account I/Q imbalance effects. In addition, we propose a low-complexity pilot-aided scheme for the estimation of the channel and I/Q imbalance parameters. Our simulation results demonstrate the effectiveness of the proposed generalized beamforming scheme in mitigating I/Q imbalance effects.

In this paper, we derive an analytical expression for the subcarrier average SINR of a beamforming OFDM system in the presence of joint transmit-receive I/Q imbalance. The derived expression quantifies the impact of the I/Q imbalance level at both the transmit and receive sides, the size of the beamforming array, and the input SNR level on performance. Several special and limiting cases are investigated providing new design insights.

In this paper, we derive an analytical expression for the error vector magnitude (EVM) under joint transmit-receive Inphase-Quadrature imbalance (IQI). IQI is a major radio frequency impairment in direct- conversion transceivers. We consider multi-user uplink transmission with single-carrier frequency- division-multiple access as in the long-term evolution standard. The derived EVM expression reveals an interesting relationship between the spectral allocation of a user's subcarriers and the immunity of this user to IQI. Furthermore, we verify the accuracy of our derived EVM expression through Monte-Carlo simulations for various IQI levels.

We derive an analytical expression for the error vector magnitude under carrier frequency offset (CFO) and joint transmit-receive phase noise (PN). CFO and PN are major oscillator impairments in direct-conversion transceivers. As a case study, we consider multi-user uplink transmission with single- carrier frequency-division-multiple access as in the long-term evolution standard. The derived expression reveals an interesting relationship between the subcarrier mapping scheme and the immunity to CFO and PN. Furthermore, we verify the accuracy of our derived expression through Monte-Carlo simulations for various CFO and PN levels.

We propose two novel algorithms based on compressive sensing theory to estimate and cancel narrow band interference (NBI) and estimate the channel in the presence of NBI for multiple-antenna orthogonal frequency division multiplexing systems. We also consider the case of multiple asynchronous NBI signals experiencing unknown independent frequency- selective fading channels. Simulation results demonstrate the effectiveness of our proposed algorithm in mitigating NBI and approaching the interference-free performance limit over practical ranges of NBI power and spectral widths.

TD-SCDMA (Time Division- Synchronous CDMA) systems show performance degradation due to I/Q imbalance resulting from direct-conversion transmitters and receivers. In overall system analysis, it is important to consider both transmitter and receiver I/Q imbalances to account for low cost base stations (BS's) such as femtocells. Two approaches are considered in this paper. The first one is a purely time-domain approach based on widely linear minimum mean square error estimation (WL-MMSE). The second one is a frequency-domain approach with reduced computational complexity. Simulation studies are performed and comparison results presented.

Under high mobility, the orthogonality between sub-carriers in an OFDM symbol is destroyed resulting in severe inter-carrier interference (ICI). We present a novel algorithm to estimate the channel and ICI coefficients by exploiting the channel's time and frequency correlations and the (approximately) banded structure of the frequency-domain channel matrix. In addition, we invoke the asymptotic equivalence of Toeplitz and circulant matrices to reduce the dimensionality of the channel estimation problem by retaining the dominant terms only in an offline eigen-decomposition. Furthermore, we show that the asymptotically MMSE-optimum pilot design consists of identical equally-spaced frequency-domain clusters whose size is determined by the channel Doppler spread. Comparisons of our proposed algorithm with a widely-cited recent algorithm demonstrate a significant performance advantage at a comparable real-time complexity.

In this paper, we present a new approach to design sparse finite impulse response (FIR) equalizers. We consider linear equalizers, decision feedback equalizers (DFEs), and multiple-input multiple-output DFEs. We formulate greedy and convex-optimization-based solutions for sparse FIR equalizers given a maximum allowable loss in the decision-point signal-to-noise ratio. Simulation results demonstrate substantial reductions in the number of equalizer taps at small performance loss.

This paper addresses spectrally-efficient multiantenna multi-carrier uplink transmission scenarios where the users overlap in time and frequency and are separated using spatial processing at the base station. The robustness of the proposed training sequences to residual carrier frequency offset and phase noise is evaluated analytically. This analysis reveals an interesting design tradeoff between the Peak-to-Average Power Ratio of a training sequence and the increase in channel estimation mean squared error over the ideal case when these two impairments are not present.

In this paper, we propose a new framework for the design of sparse finite impulse response (FIR) equalizers. We start by formulating greedy and convex-optimization-based solutions for sparse FIR linear equalizer tap vectors given a maximum allowable loss in the decision-point signal-to-noise ratio. Then, we extend our formulation to decision feedback equalizers and multiple-antenna systems. This is followed by further generalization to the channel shortening setup which is important for communication systems operating over broadband channels with long channel impulse responses. We propose a novel approach to design a sparse target impulse response. Finally, as an application of current practical interest, we consider self far-end crosstalk cancellation on vectored very high-speed digital subscriber line systems for cellular backhaul networks.

We consider comb-type OFDM transmission over doubly-selective channels. Given a fixed number and total power of the pilot subcarriers, we show that the MMSE-optimum pilot design consists of identical equally-spaced clusters where each cluster is zero-correlation-zone sequence.

We investigate the performance of orthogonal frequency division multiplexing (OFDM)-based dual-hop amplify-and-forward (AF) relay networks in the presence of phase noise (PHN). We show that the use of an AF relay may not be beneficial compared to a direct transmission in the presence of PHN. Using outage probability analysis, an upper bound on the allowable PHN level is derived which ensures that the dual-hop outperforms the direct transmission. To improve dual-hop transmission performance in the presence of PHN, we propose a reduced-complexity joint channel and PHN estimator using full-pilot OFDM symbols for AF relay transmission. The proposed approach achieves a lower mean squared error compared to the conventional channel estimator. In addition, we derive a joint data detection and PHN estimation scheme for comb-type data OFDM symbols by modifying the maximum ratio combining metric to account for the effect of PHN at the destination node.

In frequency-selective channels linear receivers enjoy significantly-reduced complexity compared with maximum likelihood receivers at the cost of performance degradation which can be in the form of a loss of the inherent frequency diversity order or reduced coding gain. This paper demonstrates that the minimum mean-square error symbol-by-symbol linear equalizer incurs no diversity loss compared to the maximum likelihood receivers. In particular, for a channel with memory ν, it achieves the full diversity order of (ν+1) while the zero-forcing symbol-by-symbol linear equalizer always achieves a diversity order of one.

In this paper, we design MMSE-optimal training sequences for multi-user MIMO-OFDM systems with an arbitrary number of transmit antennas and an arbitrary number of training symbols. It addresses spectrally-efficient uplink transmission scenarios where the users overlap in time and frequency and are separated using spatial processing at the base station. The robustness of the proposed training sequences to residual carrier frequency offset and phase noise is evaluated. This analysis reveals an interesting design tradeoff between the peak-to-average power ratio of a training sequence and the increase in channel estimation mean squared error over the ideal case when these two impairments are not present.

In this paper, we propose an efficient receiver design for single carrier frequency-domain equalization (SC-FDE) for relay-assisted transmission scenario over frequency selective channels. Building upon our earlier work, we propose a novel minimum mean square error (MMSE)-based receiver design tailored to broadband cooperative networks. We show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains, while maintaining low complexity implementation. Specifically, under the assumption of perfect power control and high signal-to-noise ratio (SNR) for the underlying links and assuming either of source-to-relay (S → R) or relay-to-destination (R → D) links to be frequency selective Rician fading, our performance analysis demonstrates that the proposed receiver is able to achieve a maximum diversity order of min (LSR,LRD) + LSD+ 2, where LSR, LRD, and LSDare the channel memory lengths for S → R, R → D, and source-to-destination (S → D) links, respectively. Simulation results demonstrate that our proposed receiver outperforms the conventional cooperative MMSE-SC-FDE receiver by performing close to the matched filter bound (MFB).

In this paper, we present a novel approach based on compressive sensing theory to estimate and mitigate asynchronous narrow-band interference (NBI) in orthogonal frequency division multiplexing systems with multiple transmit and/or multiple receive antennas. We consider the practical scenarios where one or multiple asynchronous NBI signals experience fast fading and/or frequency-selective fading channels. Furthermore, we propose a novel technique for estimating the desired signal's channel in the presence of unknown NBI. Our approach does not require any prior information about the NBI. Simulation results demonstrate the effectiveness of our proposed techniques in mitigating NBI and approaching the interference-free performance limit over practical ranges of NBI power levels, spectral widths, and mobility levels.

In this paper, we propose an efficient receiver design for single carrier frequency-domain equalization (SCFDE) for relay-assisted transmission scenario over frequency selective channels. Building upon our earlier work, we propose a novel minimum mean square error (MMSE)-based receiver design tailored to broadband cooperative networks. We show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains, while maintaining low complexity implementation. Specifically, under the assumption of perfect power control and high signal-to-noise ratio (SNR) for the underlying links and assuming either of S → R or R → D links to be frequency selective Rician fading, our performance analysis demonstrates that the proposed receiver is able to achieve a maximum diversity order of min(LSR, LRD) + LSD+2, where LSR, LRD, and LSDare the channel memory lengths for S → R, R → D, and S → D links, respectively. Complexity analysis and simulation results demonstrate that our proposed receiver outperforms the conventional cooperative MMSE-SC-FDE receiver by performing close to matched filter bound (MFB), while providing minimal computational complexity.

The complexity of maximum-likelihood (ML) or maximum-a-posteriori (MAP) detectors grows exponentially with the number of channel impulse response (CIR) taps. This makes the implementation of ML or MAP detectors over broadband channels with long CIRs prohibitively complex. Channel shortening is a widely-used technique to solve this problem by implementing a front-end finite impulse response (FIR) filter to shorten the CIR. In this paper, we propose a novel approach based on compressive sensing theory to design low-complexity FIR channel shortening filters. The superiority of our new approach is proven analytically and illustrated via simulations.

We propose a novel algorithm based on compressive sensing (CS) theory to estimate narrow band interference (NBI) signals experiencing time-varying frequency-selective fading channels in orthogonal frequency division multiplexing (OFDM) systems. In addition, we investigate the case of asynchronous jamming where there is a frequency offset between the NBI and desired signals. Furthermore, we propose a reduced-complexity implementation for our proposed algorithm with negligible performance loss. Finally, we show that our proposed approach can be applied to both cyclic-prefix and zero-padding OFDM systems. Simulation results show the effectiveness of our proposed algorithm in mitigating NBI.

We propose two novel algorithms based on compressed-sensing theory to estimate and cancel narrow band interference (NBI) in orthogonal frequency division multiplexing (OFDM) systems. Simulation results demonstrate the effectiveness of our proposed algorithms in estimating the NBI frequency support and approaching the performance with no NBI.

Single-carrier frequency-division multiple-access (SC-FDMA) has been adopted in the uplink of the LTE standard due to its lower peak-to-average-power ratio (PAPR) compared to orthogonal frequency-division multiple access (OFDMA). Recent activities in the LTE-advanced (LTE-A) standardization have focused on effective uplink transmit diversity schemes with low PAPR but without considering the performance degradation due to Doppler despite the fact that LTE-A is required to support high mobility. In this paper, we present an open-loop space-frequency block coding (SFBC) scheme for the SC-FDMA uplink and demonstrate its robustness to high Doppler and large multipath delay spread while enjoying full spatial diversity, low PAPR and practical decoding complexity by a suitable design of the frequency span of each SFBC codeword. In this paper, we study the design tradeoffs involved in SFBC design for mobile SC-FDMA.

In our previous work, digital baseband compensation of joint Tx/Rx I/Q imbalance in mobile SC-FDE transceivers was investigated. In this sequel, the training sequence design for joint channel and I/Q imbalance parameter estimation in mobile SC-FDE transceivers is discussed in greater detail.

In this sequel to [1], the joint Tx/Rx I/Q imbalance model of mobile SC-FDE transceivers is extended to include the effect of residual carrier frequency offset. Furthermore, the equivalent channel model for multiple-antenna spatially-multiplexed SC-FDE transceivers in the presence of I/Q imbalance and mobility is derived and an effective digital baseband compensation scheme is proposed, and its performance is evaluated including the effects of channel estimation.

In this paper, we propose a robust non-iterative pilot-aided channel estimator for multi-input multi-output orthogonal frequency division multiplexing systems in the presence I/Q imbalance and phase noise. We show that by exploiting the phase noise and I/Q imbalance properties, the joint cost function for estimating them reduces to separate cost functions, each of which can be readily solved. Mean-squared-error performance of the proposed estimator is evaluated for IEEE 802.11n wireless local area network applications.

In this paper, the bit error rate performances of multi-input multi-output orthogonal frequency division multiplexing transmission schemes for two and four transmit antennas are studied and compared in the presence of phase noise, I/Q imbalance and channel estimation errors. It is shown that for matched rates and same decoding complexity, maximum diversity schemes such as the Alamouti code, perform better than the maximum capacity schemes such as spatial multiplexing. The simulation environment comply with IEEE 802.11n standard.

Zero-Padding Orthogonal Frequency Division Multiplexing (ZP-OFDM) has been proposed as an alternative solution to coded-OFDM, which often incurs high decoding complexity. Various ZP-OFDM receivers have been proposed in the literature, exchanging performance with complexity. On the contrary, we propose in this paper a novel reduced-complexity minimum mean square error (MMSE)-based receiver for ZP-OFDM transmissions without sacrificing performance. We demonstrate that the proposed receiver enjoys a remarkably simple decoding scheme, while outperforming conventional ZP-OFDM-MMSE in terms of symbol error rate (SER) performance. We further show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains.

In this paper, we propose a new detection scheme for single carrier frequency-domain equalization (SC-FDE) for relay-assisted transmission scenario over frequency selective channels. We show that, by incorporating linear processing techniques, our new receiver significantly outperforms the minimum mean square error (MMSE)-distributed (D)-SC-FDE receiver in terms of the error rate performance. Simulation results and complexity analysis demonstrate that our proposed receiver outperforms the conventional cooperative MMSE-SC-FDE receiver by performing close to matched filter bound (MFB), while incurring a minimal additional computational complexity.

Direct-conversion multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transceivers enjoy high data rates and reliability at practical implementation complexity. However, analog front-end impairments such as I/Q imbalance and high mobility requirements of next-generation broadband wireless standards result in performance-limiting inter-carrier interference (ICI). In this paper, we study the effects of ICI due to these impairments for OFDM with space frequency block codes and spatial multiplexing, derive a generalized linear model and propose a non-iterative reduced-complexity digital baseband joint compensation scheme. Furthermore, we present a pilot scheme for joint estimation of the channel and the I/Q imbalance parameters and evaluate its performance through simulations. Our proposed scheme is effective in estimating and compensating for frequency-independent and frequency-dependent transmit and receive I/Q imbalances even in the presence of a residual frequency offset.

The maximum likelihood estimate of the impulse response of a frequency-selective channel in the presence of phase noise and I/Q imbalance is derived. The complexity of the joint estimator is reduced using approximate cost functions for both phase noise and I/Q imbalance. The proposed estimator is first applied to OFDM transmission in a single-input single-output system and then generalized to multi-input multi-output OFDM systems. The bit error rate performance of popular space-time codes for two and four transmit antennas is evaluated under zero-forcing, minimum mean squared error and maximum likelihood detection rules. An expression for the residual inter-carrier interference variance after phase noise and I/Q imbalance compensation is derived and compared to the uncompensated case. Significant improvement in signal-to-interference-noise is obtained with the proposed algorithm.

In this paper, we propose a novel low complexity two-stage minimum mean square error (MMSE)-based receiver for single carrier frequency-domain equalization (SC-FDE) for space-time block coded (STBC) transmissions over frequency selective channels. We demonstrate that the proposed receiver enjoys a remarkably simple decoding scheme. We further show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains, while maintaining low complexity, thus, eliminating the need for maximum-likelihood sequence detection (MLSD), which has certainly prohibitive complexity, specially, when the constellation size of the transmitted signals and/or the block length increases. Simulation results demonstrate that our proposed receiver significantly outperforms the conventional SC-MMSE-FDE receiver, while maintaining nearly similar complexity.

A new method for detecting packet-based OFDM signals in the presence of phase noise is presented. The scattered pilots in each data OFDM symbol are used to non-iteratively estimate and mitigate the phase noise induced inter-carrier interference (ICI). A significant improvement in the bit error rate (BER) is achieved using this technique, especially for systems suffering from high phase noise.

In this paper, we derive the maximum likelihood (ML) channel estimate in the presence of phase noise (PHN) and I/Q imbalance for OFDM systems. Since it is estimated jointly with I/Q and PHN, the channel estimate is more robust to these RF impairments compared to the conventional schemes. The complexity of the joint estimator is reduced using an approximate cost functions for both PHN and I/Q imbalance. The performance of the proposed estimator in terms of both channel mean squared error (MSE) and bit error rate (BER) is also presented and compared to the case of perfect channel knowledge.

In this paper, we investigate polynomial expansion approximation to reduce matrix inversion complexity as encountered in the design of the minimum mean squared error decision feedback equalizer (MMSE-DFE). The scaling factor needed in this polynomial expansion is optimized for a fixed polynomial approximation order so that the received signal to interference plus noise ratio (SINR) is maximized. The BER performance of the reduced-complexity MMSE-DFE is comparable to that of the direct matrix inversion based MMSE-DFE and outperforms earlier approaches reported in the literature.

We design a novel class of space-time codes, called linear diversity-embedding space-time block codes (LDE-STBC) where a high-rate STBC is linearly superimposed on a highdiversity STBC without requiring channel knowledge at the transmitter. In applying this scheme to multimedia wireless communications, each traffic type constitutes a transmission layer that operates at a suitable rate-diversity tradeoff point according to its quality-of-service requirements. This, in turn, provides an unequal-error-protection (UEP) capability to the different information traffic types and allows a form of wireless communications where the high-rate STBC opportunistically takes advantage of good channel realizations while the embedded high-diversity STBC ensures that at least part of the information is decoded reliably. We investigate transceiver design issues specific to LDE-STBC including reduced-complexity coherent decoding and effective schemes to vary the coding gain to further enhance UEP capabilities of the code. Furthermore, we investigate the application of LDE-STBC to wireless multicasting and demonstrate its performance advantage over conventional equal-error-protection STBC.

Multiuser systems can provide multiuser diversity gains by assigning channels to users with higher channel gains. To avoid the extensive information exchange with the access point for the uplink access in centralized approaches, we propose in this paper a distributed opportunistic access scheme. Through a judicious design of a novel backoff mechanism to utilize the channel information and reduce collisions, significant multiuser diversity gains are achieved. To a user, the higher the channel gain is, the smaller the backoff time-slot and, hence, the higher the access priority of that user is. In addition, for heterogeneous systems, our proposed scheme can realize multiuser diversity gains and achieve fairness among the users at the same time. Finally, we design two distributed opportunistic access schemes for OFDMA systems. Users contend on all sub-channels in the first scheme and only on several strongest sub-channels in the second scheme. Compared with traditional centralized OFDMA systems and other distributed access schemes, our proposed schemes reduce overhead and achieve a higher throughput.

Cyclic delay diversity (CDD) is a low-complexity standard-conformable transmit diversity scheme for coded orthogonal frequency division multiplexing (OFDM) systems. However, it makes channel estimation more challenging due to the increased frequency-selectivity of the equivalent single-input single-output channel. In this paper, we propose a novel CDD-OFDM scheme with pilot-aided channel estimation for any number of transmit antennas. By alternating and optimizing the cyclic delay parameter over adjacent OFDM symbols, we design a simple yet efficient channel estimation scheme and illustrate its excellent performance for the DVB-T application.

We propose a new full-rate space-time block code (STBC) for two transmit antennas which can be designed to achieve maximum diversity or maximum capacity while enjoying optimized coding gain and reduced-complexity maximum-likelihood (ML) decoding. The maximum transmit diversity (MTD) construction provides a diversity order of 2Nrfor any number of receive antennas Nrat the cost of channel capacity loss. The maximum channel capacity (MCC) construction preserves the mutual information between the transmit and the received vectors while sacrificing diversity. The system designer can switch between the two constructions through a simple parameter change based on the operating signal-to-noise ratio (SNR), signal constellation size and number of receive antennas. Thanks to their special algebraic structure, both constructions enjoy low-complexity ML decoding proportional to the square of the signal constellation size making them attractive alternatives to existing full-diversity full-rate STBCs in [6], [3] which have high ML decoding complexity proportional to the fourth order of the signal constellation size. Furthermore, we design a differential transmission scheme for our proposed STBC, derive the exact ML differential decoding rule, and compare its performance with competitive schemes. Finally, we investigate transceiver design and performance of our proposed STBC in spatial multiple-access scenarios and over frequency-selective channels.

The reliability of mobile packet data transmission in multi-input multi-output (MIMO) communication systems is degraded by time-varying multi-path channel fading. This so-called Doppler-effect can be mitigated by using a shorter packet length and/or inserting more pilot training symbols and frequently tracking the channel at the receiver at the cost of additional rate overhead. A judicious choice of the packet length according to channel conditions aims at achieving a tradeoff between reliability and overhead to maximize the achievable throughput. We present an analytical framework for optimizing the packet length in mobile MIMO systems to maximize throughput taking into account channel estimation and Doppler effects. As a case study, we consider the Alamouti space-time block code (STBC) for 2 transmit antennas over Rayleigh flat-fading channels and determine the optimum packet length as a function of input SNR and Doppler rate. Furthermore, we investigate the effect of various system design parameters on the optimum packet length and achievable throughput such as signal constellation size, number of receive antennas, and receiver detection algorithm.

In-phase and quadrature-phase (I/Q) imbalance is a major performance-limiting impairment for direct- conversion orthogonal frequency division multiplexing (OFDM) transceivers. I/Q imbalance degrades the signal-to-noise-ratio in an OFDM system by causing inter-channel-interference (ICI) between image subcarriers. Doppler spread due to mobility also causes ICI but mainly between adjacent subcarriers. In this work, we investigate the combined effects of mobility and I/Q imbalance in a multiple-input-multiple-output-OFDM system with the alamouti space-frequency block-coding scheme and design an effective compensation technique. Furthermore, we propose a novel pilot allocation and channel estimation scheme, and exploit the problem structure to reduce complexity.

In this paper, we propose a generalized utility function model for cooperative slotted Aloha in a distributed system. Multiuser diversity gains are achieved by appropriately designing the Nash equilibrium thresholds for the selfish users. The network enforces fairness among different users by employing a pricing policy that favors equal access probabilities. This generalized game model provides the flexibility in designing the utility function associated with QoS. Our study on the impact of different utility function models shows that the multiuser diversity gains achieved by different models remain constant under the throughput optimization framework and vary slightly under the revenue optimization framework.

In cellular networks, a mobile station (MS) may be located in the coverage hole or out of the coverage of the base station (BS). In such a scenario, one or more relay stations (RSs) can be used to facilitate the transmission. Here, a threshold- based opportunistic cooperative ARQ transmission scheme is proposed. Based on this scheme, the transmission between the BS and the MS can be separated into two parts: firstly, the transmission between BS and RSs (BS-RSs link) and secondly, the transmission between RSs and MS (RSs-MS link). The proposed scheme is different from the conventional ACK/NACK used for unicast transmission, in which two new types of ARQ messages are introduced for multicast transmission, namely, the relay-associated ACK/NACK (i.e., R-ACK/R-NACK) and the cooperative ACK/NACK (i.e., C-ACK/C-NACK), for BS-RSs link and RSs-MS link, respectively. A pre-defined threshold is applied to evaluate the reliability of the BS-RSs link. If the number of reliable RSs is larger than the threshold value, the reliable RSs will transmit the packet to the MS in a cooperative manner. Since reliable transmission exists in the BS-RSs link, the BS will release the packet and further transmission of this packet occurs only between reliable RSs and the MS. Therefore, the proposed scheme can reduce the latency of cellular networks and the processing burden on the BS as well as achieve higher throughput as shown in the performance analysis and simulation results.

In this paper, we compare the downlink capacity and throughput between the variable spreading factor-orthogonal frequency and code division multiple access (VSF-OFCDMA) with one-cell frequency reuse, and the orthogonal frequency division multiple access (OFDMA) with conventional three-cell frequency reuse. Since the multiuser diversity gain in OFDMA systems can be achieved by approximately designing its access schemes, while it is non-trivial for the VSF-OFCDMA systems, the effect of both access schemes on the link capacity are investigated. Our simulation results reveal that although the multiuser diversity gain achieved by OFDMA systems can compensate the loss of low frequency reuse at low to mid range of signal-to-interference-plus-noise ratio (SINR) values, the achievable throughput of VSF-OFCDMA systems is much higher than the OFDMA systems. Furthermore, the impact of user’s location on the inter-cell-interference is also reported.

Effective inter-carrier interference (ICI) mitigation for MIMO-OFDM requires accurate channel estimation which is very challenging due to the large number and fast time-varying nature of the channel parameters to be estimated using scattered pilots. We present a novel SFBC-OFDM scheme for doubly-selective channels and a reduced complexity channel estimation algorithm which exploits the banded and sparse structure of the channel in the frequency and time-domains, respectively. Furthermore, we design an FIR-MMSE ICI cancellation algorithm for mobile SFBC-OFDM and demonstrate its effectiveness for digital video broadcasting-handheld (DVB-H) systems.

I/Q imbalance and high mobility in OFDM systems result in performance-limiting intercarrier interference (ICI). However, the nature of ICI due to each of them is quite different. Unlike previous works which considered these two impairments separately, we develop a unified mathematical framework to characterize and mitigate ICI when both impairments are present. In addition, we exploit the special ICI structure to design efficient OFDM channel estimation and digital baseband compensation schemes for I/Q imbalance under high-mobility conditions.

Diversity-embedded space-time block coding (DE- STBC) introduced in [1] is a class of non-orthogonal STBC which can provide multiple diversity orders and transmission rates for the information symbols. This is achieved by transmitting the information symbols in layers each characterized by a diversity-rate tuple according to its QoS requirement. This paper derives a closed-form performance bound for DE-STBC over flat fading channels using outage probability analysis. Furthermore, the effective coding gain and system throughput are analyzed and optimized based on the derived outage expressions.

Rate and diversity impose a fundamental tradeoff in wireless communication. High-rate space-time codes come at a cost of lower reliability (diversity), and high reliability (diversity) implies a lower rate. However, wireless networks need to support applications with very different quality-of-service (QoS) requirements, and it is natural to ask what characteristics should be built into the physical layer link in order to accommodate them. In this paper, we design high-rate space-time codes that have a high-diversity code embedded within them. This allows a form of communication where the high-rate code opportunistically takes advantage of good channel realizations while the embedded high-diversity code provides guarantees that at least part of the information is received reliably. We provide constructions of linear and nonlinear codes for a fixed transmit alphabet constraint. The nonlinear constructions are a natural generalization to wireless channels of multilevel codes developed for the additive white Gaussian noise (AWGN) channel that are matched to binary partitions of quadrature amplitude modulation (QAM) and phase-shift keying (PSK) constellations. The importance of set-partitioning to code design for the wireless channel is that it provides a mechanism for translating constraints in the binary domain into lower bounds on diversity protection in the complex domain. We investigate the systems implications of embedded diversity codes by examining value to unequal error protection, rate opportunism, and packet delay optimization. These applications demonstrate that diversity-embedded codes have the potential to outperform traditional single-layer codes in moderate signal-to-noise (SNR) regimes.

We develop a reduced-complexity hybrid frequency/time-domain orthogonal frequency division multiplexing (OFDM) channel estimation algorithm for high-mobility scenarios where the channel varies significantly within each OFDM block, resulting in severe intercarrier interference (ICI). The algorithm exploits the banded and symmetric structure of the channel matrix in the frequency domain and its sparse structure in the time-domain to achieve significant complexity reductions, which we quantify for the Digital Video Broadcasting-Handheld (DVB-H) system. Furthermore, we compare coherent and differential mobile OFDM detection for DVB-H.

Training signals for OFDM channel estimation should possess low PAR to avoid nonlinear distortions at the transmit amplifier and at the same time they should be robust against frequency offsets. In this letter, we show that the above two requirements of the training signals are conflicting. We propose two new training signal designs for MIMO OFDM frequency-selective channel estimation. Our proposed training signals achieve more robust channel estimation performance against frequency offsets while satisfying the PAR constraints compared to training signals designed to achieve a fixed low PAR but without any consideration for robustness to frequency offsets.

Diversity-embedding space-time block coding (DE-STBC), introduced in the work of Diggavi et al. (2003), enables unequal error protection (UEP) using multiple transmit antennas. Even though these codes do not require channel state information (CSI) at the transmitter, they do need it at the receiver for decoding. A novel differential DE-STBC scheme is proposed in this paper to eliminate the need for channel estimation at the receiver which is especially costly with multiple transmit and receive antennas. Most previously proposed differential schemes in the literature are based on orthogonal STBC and hence are not applicable to the non- orthogonal family of DE-STBC considered in this paper.

Multiuser diversity gains can be achieved by assigning channels to users with better channel quality in multiuser systems. To avoid the extensive information exchange required for centralized approaches, we propose a distributed fair pricing strategy for a slotted Aloha system in which users act selfishly to improve their own utilities for both the collision model and the multipacket reception (MPR) model. Based on a game theoretic framework, we show that multiuser diversity gains can be achieved by appropriately designing the Nash equilibrium thresholds for the selfish users to preserve the throughput and revenue achieved in the classical slotted Aloha systems. The network enforces fairness among different users by employing a pricing policy that favors equal access probabilities. Our simulation results show that significant multiuser diversity gains are achieved in terms of energy consumption and/or spectral efficiency.

Opportunistic space-time block codes (STBC) have been investigated for flat-fading channels in the literature. In this paper, we present a novel scheme for integrating them with orthogonal frequency division multiplexing (OFDM) for frequency-selective channels with an unequal error protection capability making it attractive for multimedia and multicasting applications. In addition, our new scheme optimizes the tradeoff between coding gain and peak-to-average ratio (PAR) while minimizing inter-carrier interference in the presence of carrier frequency offset. We show that our scheme achieves a 2 dB reduction in PAR over the conventional scheme.

In this paper, we propose a robust STBC transmission scheme to combat timing synchronization errors over frequency-selective multiple-access channels. We start by deriving the equivalent channel model in the presence of timing synchronization errors. Based on this correlated equivalent channel model, the statistical channel power gain profile is analyzed and shown to exhibit unequal channel power gains on different subcarriers. Furthermore, a robust statistical bit loading algorithm is proposed to optimize the BER performance in scenarios where link adaptation based on instantaneous channel information is infeasible or undesirable. Simulation results show that our proposed scheme is robust to timing synchronization errors, providing around 7.5 dB SNR advantage at the BER of 10-4over the conventional scheme in various channel environments.

In [(S. Diggavi et al., 2003),(S. Das et al., 2005, 2006)] opportunistic space-time block codes were presented for flat-fading channels. In this paper, we propose an opportunistic STBC-OFDM for frequency-selective channels with an unequal error protection capability making it attractive for multimedia applications. Our new scheme optimizes the tradeoff between coding gain and peak-to-average ratio while minimizing inter-carrier interference in the presence of carrier frequency offset. We show that our scheme achieves a 2 dB reduction in PAR over the conventional scheme.

In multiuser systems, multiuser diversity gains can be achieved by assigning channels to users with higher channel quality. To avoid the extensive information exchange required for centralized approaches, we propose a distributed fair pricing strategy for a slotted ALOHA system in which users act selfishly to improve their own utilities. Based on a game theoretic framework, we show that multiuser diversity gains can be achieved by appropriately designing the Nash equilibrium thresholds for the selfish users, such as to preserve the throughput and revenue achieved in the centralized schemes. The network enforces fairness among different users by employing a pricing policy that favors equal access probabilities. Our simulation results show significant multiuser diversity gains achieved in terms of energy consumption and/or spectral efficiency.

In this paper, we investigate the effect of quantization noise and roundoff errors involved in finite-precision (FP) signal processing on the performance of multiple-input multiple-output (MIMO) receivers under flat-fading channel conditions. We simulated the performance degradation with FP for six transmission schemes, namely the Alamouti transmit diversity scheme, spatial multiplexing (SM) under maximum-likelihood (ML) detection, SM under ordered successive interference cancellation (OSIC) for (2 times 2) MIMO, orthogonal space-time block code (STBC), quasi-orthogonal STBC for (4 times 2) MIMO and interference cancellation with two (2 times 2) Alamouti scheme. We also provide a procedure to analyze roundoff errors in FP MIMO receivers and quantify the effective decision-point SNR. The quantified variance of accumulated quantization noise and decision-point SNR for FP Alamouti scheme corroborates the simulation result. Finally, we provide the minimum word-length precision requirements for these schemes.

This paper investigates the diversity order of single-carrier, symbol-by-symbol linear equalization (LE). It is shown that minimum mean square error (MMSE) linear equalizers achieve full diversity of v + 1 (the number of channel taps) independent of spectral efficiency. Our results also provide a new proof for the full diversity of decision feedback equalization (DFE), which was shown originally in A. Medles and D.T.M Slock (2004).

A new information lossless, full-rate space-time block code is presented for 2 transmit antennas over 2 symbol periods. We show that our code is capacity-achieving and exploit its special algebraic structure to derive a reduced-complexity maximum-likelihood (ML) decoding algorithm. When the number of receive antennas is greater than 1, our code outperforms the Alamouti code. Comparing the performance of our code with state-of-the-art B2Phiand Golden codes in [4] and [7], demonstrates competitive performance for practical low-to-medium SNR ranges, for high spectral efficiencies, and as the number of receive antennas increases while requiring much lower ML decoding complexity.

This paper presents pilot designs for consistent frequency-offset estimation of orthogonal frequency-division multiplexing systems in frequency-selective fading channels. We describe two design approaches, namely, consistency in the probabilistic sense and absolute consistency. Existing preambles and pilot designs in the literature do not guarantee the absolute consistency. We derive general criteria for both approaches, present sufficient conditions on the pilot structures over the maximum carrier frequency offset (CFO) estimation range (half of the sampling rate), and derive simple pilot designs satisfying these conditions. We also extend the sufficient conditions to any arbitrary but fixed CFO estimation range, and present some generalized design patterns. Furthermore, the CFO estimation performances of distinct consistent pilot designs can be quite different at moderate or low signal-to-noise ratio (SNR) due to different statistics of outliers which also yields a link failure. We develop efficient pilot-design criteria that provide both consistency and robustness against outliers at moderate-to-low SNR. Our consistent pilot designs facilitate flexible and economical implementation, while our robust pilot designs enable wireless links with less outage and better resilience

In this paper, we propose a distributed opportunistic access scheme for uplink OFDMA systems. The sub-carriers are grouped into several sub-channels and users access those sub-channels through a distributed access scheme without requiring extensive information exchange with the access point. Our proposed scheme allows several parallel contention subchannels to exploit multiuser diversity. Using knowledge of all the sub-channels, obtained through a periodically-transmitted beacon signal from the access point, all users contend on their strongest sub-channel(s). The proposed scheme applies a novel backoff mechanism utilizing this sub-channel knowledge to yield further throughput improvement in addition to the throughput gain obtained by the collision reduction design. To a user, the better the sub-channel gain is, the smaller the backoff time on that sub-channel, and hence, the higher the access priority of the user on that sub-channel. Compared with the traditional centralized OFDMA systems, our proposed scheme reduces overhead significantly and achieves a better spectral efficiency as corroborated by the simulation results.

In this paper, we address the use of a novel low-complexity receiver, namely, a reduced-complexity hybrid receiver (HR) for the downlink of a multicell CDMA system with a transmit delay diversity transmission scheme. This receiver is designed by combining the merits of the decorrelating receiver (DR) and the conventional receiver (CR). Unlike most multiuser receivers, the reduced-complexity HR operates with the same information as CR. For a target performance metric (e.g. bit error probability (BEP) = 10-2), the reduced-complexity HR significantly outperforms CR, DR, average minimum mean square error (MMSE) receiver, signal to noise ratio (SINR) maximizing (max-SINR) receiver, and reduced-complexity MMSE receiver with estimated channel information.

In this paper, we investigate time-domain equalization for distributed space-time codes in a relay-assisted transmission scenario over frequency-selective fading channels. Specifically, we consider the so-called time-reversal space-time block coding (TR-STBC) technique which has been of particular interest with its low computational complexity. We assume the special case of a single-relay where the source-to-relay (S → R), relay-to-destination (R → D), and source-to-destination (S → D) links experience possibly different channel delay spreads. Under the assumption of perfect power control for the relay terminal and high signal-to-noise ratio for the underlying links, our performace analysis demonstrates that distributed TR-STBC is able to achieve a maximum diversity order of min (L1, L3) +L2 + 2 where L1, L2, and L3 are the channel memory lengths for S → R, S → D, and R → D links, respectively. This illustrates that the smaller of the multipath diversity orders experienced in S → R and R → D links becomes the performance bottleneck for the relaying path. An extensive Monte Carlo simulation study is presented to corroborate the analytical results and to provide detailed performance comparisons with competing schemes.

In this paper, we investigate orthogonal frequency division multiplexing (OFDM) technique for distributed space-time block coding (D-STBC) in a relay-assisted transmission scenario over frequency-selective fading channels. We consider the special case of a single-relay where the source-to-relay (S - R), relay-to-destination (R - D) and source-to-destination (S - D) links experience possibly different channel delay spreads. Our analysis demonstrates that uncoded distributed OFDM-STBC (D-OFDM-STBC) scheme achieves a maximum diversity order of two for the considered single-relay scenario since it is able to exploit only the spatial diversity, but not the available rich multipath diversity. We further consider a combination of D-OFDM-STBC and Trellis Coded Modulation (TCM) with frequency-interleaving. Under the assumption of perfect power control for the relay terminal and high signal-to-noise ratio for the underlying links, our performance analysis demonstrates that D-OFDM-STBC scheme with judiciously designed outer TCM codes, i.e. sufficiently large effective code length (ECL), is able to achieve a maximum diversity order of min(L1, L3) + L2 + 2 where L1, L2, and L3 are the channel memory lengths for S - R, S - D and R - D links, respectively. This illustrates that the smaller of the multipath diversity orders experienced in S - R and R - D links becomes the performance bottleneck for the relaying path. An extensive Monte Carlo simulation study is presented to corroborate the analytical results and to provide further insights into the performance.

This paper presents pilot designs for consistent frequency offset estimation of OFDM systems in frequency-selective fading channels. We describe two design approaches, namely consistency in the probabilistic sense and absolute consistency. Existing preambles and pilot designs in the literature do not guarantee the absolute consistency. We derive general criteria for both approaches, present sufficient conditions on the pilot structures, and derive simple pilot designs satisfying these conditions. Absolute consistency should not be compromised in emergency-related or other critical communication scenarios and our proposed consistent pilot designs address this need.

In mobile reception, orthogonal frequency division multiplexing (OFDM) may experience significant inter-carrier interference (ICI) which introduces an error floor proportional to the normalized Doppler frequency. To mitigate the effect of ICI, many schemes have been proposed but they are computationally complex for systems using large OFDM symbol length such as digital video broadcasting system for handheld (DVB-H). In this paper, we propose a low-complexity ICI suppression and channel estimation scheme for DVB-H systems. The proposed scheme is applied to a typical DVB-H system. Simulation results demonstrate that the proposed scheme is robust to mobility and can be employed efficiently in a DVB-H system. Comparison of DVB-H system with OFDM modulation and single carrier modulation (SC) employing frequency domain equalization (FDE) is investigated. Simulation results show the superiority of the SC to OFDM modulation for DVB-H systems and for the simulated scenarios.

Frequency division multiplexing (OFDM) system may suffer from significant Inter-carrier Interference (ICI) and Inter-block Interference (IBI) under a time- and frequency- selective (or doubly-selective) channel. We propose a novel hybrid architecture where a time-domain FIR filter mitigates IBI by shortening the channel to the cyclic prefix length. This is followed by ICI cancellation in the frequency-domain using few FIR taps per subchannel combined with successive cancellation. We also propose an iterative joint data and channel estimation scheme to improve the channel estimation performance.

In this paper, we investigate single carrier frequency-domain equalization (SC-FDE) for distributed space-time block codes (D-STBC) in a relay-assisted transmission scenario over frequency-selective fading channels. We assume the special case of a single-relay where the source-to-relay (S rarr R), relay-to-destination (R rarr D), and source-to-destination (S rarr D) links experience possibly different channel delay spreads. Assuming perfect power control between R rarr D and S rarr D links and high signal-to-noise ratio for all underlying links, our performance analysis demonstrates that SC-FDE for D-STBC is able to achieve a maximum diversity order of min(L1, L3)+L2+2 where L1, L2and L3are the channel memory lengths for S rarr R, S rarr D, and R rarr D links, respectively. This illustrates that the smaller of the multipath diversity orders experienced in S rarr R and R rarr D links becomes the performance bottleneck for the relaying path. For the special case of a non-fading relaying path where line-of-sight propagation is possible in either one of these underlying links, we demonstrate that the maximum diversity orders of L1+L2+2 and L3+L2+2 are achievable assuming nonfading R rarr D and S rarr R links, respectively. An extensive Monte Carlo simulation study is presented to corroborate the analytical results and to provide detailed performance comparisons among the competing schemes

We derive on the impulse response and input signal of a frequency-selective finite impulse response channel to be diagonalized by the DCT into parallel, decoupled, and memoryless subchannels. We show how these conditions can be satisfied in a practical multicarrier transceiver through a novel design of the guard sequence and the front-end prefilter. This DCT-based design results in complete elimination of interblock and intercarrier interference without channel knowledge at the transmitter and at the same guard sequence overhead, compared with DFT-based multicarrier transceivers. Extensions to multi-input multi-output frequency-selective channels are also described. Finally, we present numerical examples from wireline and wireless communications scenarios to illustrate the viability and practicality of the DCT as a modulation/demodulation basis for baseband and passband signaling over frequency-selective channels.

We derive conditions on the impulse response and input signal of a frequency-selective finite-impulse response channel to be diagonalized by the discrete cosine transform (DCT) into parallel, decoupled, and memoryless subchannels. We show how these conditions can be satisfied in a practical multicarrier transceiver through a novel design of the guard sequence and the front-end prefilter. This DCT-based design results in complete elimination of interblock and intercarrier interference without channel knowledge at the transmitter and at the same guard sequence overhead, compared with DFT-based multicarrier transceivers. Extensions to multiinput multioutput frequency-selective channels are also described. Finally, we present numerical examples from wireline and wireless communications scenarios to illustrate the viability and practicality of the DCT as a modulation/demodulation basis for baseband and passband signaling over frequency-selective channels.

We develop robust mean-square error (MSE)-optimal training signal designs for multiple-input multiple-output orthogonal frequency-division multiplexing channel estimation with frequency offset and phase noise (PN), and present analytical and simulation results for the frequency-offset and PN effects on channel estimation. The proposed designs are more advantageous for moderate-to-high values of signal-to-noise ratio (SNR), residual frequency offset, and PN level. At SNR = 10 dB, the normalized MSE reductions of our proposed training signals at normalized frequency offset |v|=0.1,0.5 are about 9 and 19 dB, respectively, for one transmit antenna, and 6 and 11 dB for two transmit antennas

This paper presents general classes of optimal training signals for the estimation of frequency-selective channels in MIMO OFDM systems. Basic properties of the discrete Fourier transform are used to derive the optimal training signals which minimize the channel estimation mean square error. Both single and multiple OFDM training symbols are considered. Several optimal pilot tone allocations across the transmit antennas are presented and classified as frequency-division multiplexing, time-division multiplexing, code-division multiplexing in the frequency-domain, code-division multiplexing in the time-domain, and combinations thereof. All existing optimal training signals in the literature are special cases of the presented optimal training signals and our designs can be applied to pilot-only schemes as well as pilot-data-multiplexed schemes.

We consider linear detectors for MIMO systems, i.e., multi-antenna systems where linear equalizers are employed to remove spatial interference. We analyze the behavior of linear equalizers through outage probability. The MMSE equalizer was found to behave in unexpected ways. Contrary to the usual intuition, the performance of MMSE and zero-forcing equalizers may not coincide at high-SNR. This is especially true at low spectral efficiencies, where the MMSE equalizer may achieve full spatial diversity.

We derive conditions on the impulse response and input signal of a frequency-selective FIR channel to be diagonalized by the DCT into parallel, decoupled, and memoryless subchannels. We show how these conditions can be satisfied in a practical multi-carrier transceiver through a novel design of the guard sequence and the front-end prefilter. This DCT-based design completely eliminates inter-block and inter-carrier interference at a lower complexity and without incurring any additional guard sequence overhead, compared to DFT-based multicarrier transceivers. Extensions to multi-input multi-output frequency-selective channels are also described.

All existing training signal designs for channel estimation in OFDM systems assume no frequency offsets. In practice, frequency offset is unavoidable and seriously degrades the performance of OFDM systems. In this paper, we address the problem of designing optimal training signals for the estimation of frequency-selective channels in MIMO OFDM systems with frequency offsets. The mean square error advantage of our proposed optimal training signals can be quite significant for moderate-to-high values of SNR and frequency offsets.

This work presents general classes of optimal training signals for estimation of frequency-selective channels in MIMO OFDM systems. Basic properties of the discrete Fourier transform are used to derive the optimal training signals which minimize the channel estimation mean square error. Both single and multiple OFDM training symbols are considered. Several optimal pilot tone allocations among the transmit antennas are presented and classified as frequency division multiplexing, time division multiplexing, code division multiplexing in frequency-domain, code division multiplexing in time-domain, and combinations thereof. All existing optimal training signals in the literature are special cases of the presented optimal training signals and our designs can be applied to pilot-only schemes as well as pilot-data multiplexed schemes.

This paper designs space-time codes for standard PSK and QAM signal constellations that have flexible rate, diversity and require no constellation expansion. Central to this construction are binary partitions of the PSK and QAM constellations that appear in codes designed for the Gaussian channel. The space-time codes presented here are designed by separately specifying the different levels of the binary partition in the space-time array. The individual levels are addressed by either the binary symmetric matrices associated with codewords in a Kerdock code or other families of binary matrices. Binary properties of these sets are sufficient to verify the diversity property of the codewords in the complex domain. Larger sets of binary symmetric matrices (such as the set used in Delsarte-Goethals codes) are used to trade diversity protection for increased rate.

We investigate time- and frequency-domain equalization techniques for quasi-orthogonal space-time block codes. The proposed schemes extend the original symbol-level scheme developed for flat-fading channels to a block-level implementation either in the time or frequency domain. Receiver structures which exploit the embedded quasi-orthogonality, therefore keeping the equalizer complexity at a manageable level, are presented. The considered schemes are applied to the EDGE TDMA system and their performances are compared under both perfect and estimated channel conditions.

In this paper, we construct a new nonlinear 4x4 full-rate, full-diversity orthogonal space-time block code (STBC) using quaternionic algebra on which the Alamouti code is also based. We also develop a differential encoding and decoding scheme for this code, which also enjoys low decoding complexity

In this paper we present a simple scheme for subchannel allocation in OFDM multiuser networks in the presence of limited feedback, in particular, when only one bit of information per subchannel is available at the base station. Our objective is to maximize the sum rate capacity of the network in the downlink transmission. We show that even with very limited feedback the sum rate capacity growth is the same as the fully informed transmission. We also extend this result to the case when subchannels are correlated.

The effect of spatial diversity on the throughput and reliability of wireless networks is examined. Spatial diversity is realized through multiple independently fading transmit/receive antenna paths in single-user communication and through independently fading links in multiuser communication. Adopting spatial diversity as a central theme, we start by studying its information-theoretic foundations, then we illustrate its benefits across the physical (signal transmission/coding and receiver signal processing) and networking (resource allocation, routing, and applications) layers. Throughout the paper, we discuss engineering intuition and tradeoffs, emphasizing the strong interactions between the various network functionalities.

Rate and diversity impose a fundamental trade-off in space-time coding. High-rate space-time codes come at a cost of lower diversity, and high reliability (diversity) implies a lower rate. We explore a different point of view where we design high-rate space-time codes that have a high-diversity code embedded within them. This allows a form of communication where the high-rate code opportunistically takes advantage of good channel realizations whereas the embedded high-diversity code ensures that at least part of the information is received reliably. We explore this point of view with design issues, along with some preliminary progress on code constructions and some information-theoretic considerations.

We develop an efficient adaptive receiver for joint equalization and interference cancellation for multi-user space-time block-coded transmissions. The receiver exploits the rich code structure and allows multiple user transmissions over frequency-selective fading channels with reduced complexity and lower system overhead. The adaptation scheme is based on a recursive least-squares implementation for faster convergence; nevertheless, it exploits the code structure to attain RLS performance at LMS complexity.

In this paper we study the multiple-access channel where users employ space-time block codes (STBC). The problem is formulated in the context of an inter-symbol interference (IS) multiple-access channel. The algebraic structure of the STBC is utilized to design joint interference suppression, equalization, and decoding schemes. Each user transmits using 2 transmit antennas and a time-reversed space-time block code suitable for frequency-selective channels. We first show that a diversity order of 2M/sub r/(v+1) is achievable at full transmission rate for each user, when we have M/sub r/ receive antennas, channel memory of v and an optimal multi-user maximum-likelihood (ML) decoder is used. Due to the decoding complexity of the ML detectors we study the algebraic structure of linear multiuser detectors, which utilize he properties of the STBC. We do this both in the transform domain (D-domain formulation) and when we impose finite block length constraints (matrix formulation). The receiver is designed to utilize the algebraic structure of the codes in order to preserve the block quaternionic structure of the equivalent channel for each user.

We develop an efficient adaptive receiver for joint equalization and interference cancellation for multi-user space-time block-coded transmissions. The receiver exploits the code structure and allows multiple user transmission over frequency-selective fading channels with reduced complexity and lower system overhead. The adaptation scheme is based on a recursive least-squares implementation for faster convergence; nevertheless, it exploits the code structure to attain RLS performance at LMS complexity.

In this paper, we study the multiple-access channel where users employ space-time block codes (STBC). The problem is formulated in the context of an intersymbol interference (ISI) multiple-access channel which occurs for transmission over frequency-selective channels. The algebraic structure of the STBC is utilized to design joint interference suppression, equalization, and decoding schemes. Each of the K users transmits using M/sub t/=2 transmit antennas and a time-reversed STBC suitable for frequency-selective channels. We first show that a diversity order of 2M/sub r/(/spl nu/+1) is achievable at full transmission rate for each user, when we have M/sub r/ receive antennas, channel memory of /spl nu/, and an optimal multiuser maximum-likelihood (ML) decoder is used. Due to the decoding complexity of the ML detector we study the algebraic structure of linear multiuser detectors which utilize the properties of the STBC. We do this both in the transform (D-domain) formulation and when we impose finite block-length constraints (matrix formulation). The receiver is designed to utilize the algebraic structure of the codes in order to preserve the block quaternionic structure of the equivalent channel for each user. We also explore some algebraic properties of D-domain quaternionic matrices and of quaternionic circulant block matrices that arise in this study.

This paper addresses the problem of training sequence design for multiple-antenna transmissions over quasi-static frequency-selective channels. To achieve the channel estimation minimum mean square error, the training sequences transmitted from the multiple antennas must have impulse-like auto correlation and zero cross correlation. We reduce the problem of designing multiple training sequences to the much easier and well-understood problem of designing a single training sequence with impulse-like auto correlation. To this end, we propose to encode the training symbols with a space-time code, that may be the same or different from the space-time code that encodes the information symbols. Optimal sequences do not exist for all training sequence lengths and constellation alphabets. We also propose a method to easily identify training sequences that belong to a standard 2/sup m/-PSK constellation for an arbitrary training sequence length and an arbitrary number of unknown channel taps. Performance bounds derived indicate that these sequences achieve near-optimum performance.

By employing more than one antenna at the transmitter and by properly coding data across the transmit antennas, physical layers (PHYs) with space-time block codes (STBCs) promise increased data rates with minimal decoding complexity at the receiver. This paper presents a comprehensive study of how the STBC gains at the PHY translate to significant network performance improvement in 802.11a wireless local area networks. We base our study on a detailed, across-all-layers, simulation of an 802.11a system. We have extended the network simulator with an implementation of the 802.11a PHY, which allows us to assess the impact of STBC not only at the PHY layer, but at the higher layers as well. An extensive set of simulations illustrates the merits of transmit diversity (in the form of STBC) and sheds light on how performance can be improved for transmission control protocol (TCP) traffic. Essentially, STBC presents to TCP a "smoother" wireless channel; this is corroborated by a brief theoretical analysis as well.

We develop an adaptive equalization scheme for space-time block-coded (STBC) transmissions. The scheme is based on a modified low-complexity version of the fast-converging RLS algorithm. Complexity reduction is achieved by exploiting the rich structure of STBC.

We examine multicarrier transmission over time-varying channels. We first develop the model for such a transmission scheme and focus particularly on OFDM-based schemes. We analyze the impact of time-variation within a transmission block which could arise both from Doppler spread of the channel and from synchronization errors. We propose a time-domain approach to mitigate the effects of such time-variations. This approach reduces to the familiar single-tap frequency-domain equalizer when the channel is block time-invariant. We also develop this in the context of multiple transmit and receive antennas and specialize the receiver to space-time block-coded systems. Finally we provide numerical results.

We propose a method to identify training sequences for multiple-antenna transmissions over quasi-static frequency-selective channels. These sequences are constructed to belong to a standard constant-amplitude 2/sup m/-PSK constellation (such as BPSK, QPSK etc) to simplify the transmitter/receiver implementation. Many practical systems use training of predetermined length. Optimal sequences do not exist for all training sequence lengths and constellation alphabets. The proposed method allows us to identify training sequences that belong to a standard constellation for an arbitrary training sequence length and an arbitrary number of unknown channel taps. Performance bounds derived indicate that these sequences achieve near-optimum performance.

The determinant and rank criteria used for space-time code design apply at high SNR. Code design metrics developed for low SNR assume a channel autocorrelation matrix with equal eigenvalues, which does not hold in many practical scenarios. This paper shows that a space-time code designed to ensure full diversity at high SNR can suffer significant degradation when implemented at low-to-medium SNR because of the channel autocorrelation profile. We examine the effect of the channel autocorrelation matrix on a space-time code's performance and discuss how knowledge of this matrix can be used for code design, particularly from the aspect of space-time trellis code minimum memory requirements. Our discussion applies to both flat-fading and frequency-selective channels that are treated in a unified manner.

We describe key receiver signal processing algorithms for space-time-coded transmissions over broadband wireless channels. These include channel estimation, equalization, and interference cancellation. Our main theme is exploiting the rich structure of space-time codes to enhance the performance and reduce the complexity of receiver signal processing. The third generation TDMA system EDGE is taken as a case study.

We propose a scheme like that of S. Alamouti (see IEEE Journal on Selected Areas in Communications, p.1451-8, 1998) for combining space-time block-coding with single-carrier frequency-domain equalization. With 2 transmit antennas, the scheme is shown to achieve significant diversity gains at low complexity over frequency-selective fading channels.

We introduce a space-time block-coded orthogonal frequency-division multiplexing (MC-OFDM) scheme for frequency-selective fading channels which does not require channel knowledge either at the transmitter or at the receiver. The decoding algorithm is based on generalized maximum-likelihood sequence estimation. Due to the assumed orthogonality structure of STBC, the decoding rule reduces to a single step. Its form also allows the derivation of a recursive expression, which can be easily implemented by a Viterbi-type algorithm. We investigate the performance of the proposed scheme over two-tap Rayleigh fading channels. Simulation results show the performance of the proposed recursive-type receiver to be near optimum.

This paper addresses the problem of training sequence design for multiple-antenna transmissions over quasi-static frequency-selective channels. As performance metric for channel estimation, mean square error is adopted. To achieve the minimum mean square error, the training sequences transmitted from the multiple antennas must have impulse-like autocorrelation and zero crosscorrelation. We reduce the problem of designing multiple training sequences to the much easier and well-understood problem of designing a single training sequence with impulse-like auto-correlation. To this end, we propose to encode the training sequences with a space-time code, that may be the same or different from the space-time code that encodes the information symbols. Designing one instead of multiple training sequences reduces the search space significantly and simplifies the construction of optimal or suboptimal training sequences.

In this paper, we investigate OFDM transmission over fast fading channels. In such scenarios, the Doppler spread of the channel and synchronization errors cause intercarrier interference (ICI) and complicate channel estimation because the channel cannot be assumed constant during the transmission of an OFDM block. Channel estimation in rapidly time-varying scenarios becomes critical, an we propose a scheme for estimating channel parameters varying within a transmission block. Along with the channel estimation scheme, we also examine the issue of pilot tone placement and show that in time-varying channels it may be better to group pilot tones together into clumps equispaced onto the FFT grid; this placement technique is in contrast to the common wisdom for time-invariant channels. Finally we provide numerical results.

We show that the optimum length-/spl nu/ guard sequence for block transmission over a linear Gaussian-noise dispersive channel with memory /spl nu/ is a linear combination of the N information symbols of the block. A closed-form expression for the optimum guard sequence is derived subject to a total average energy constraint on the information and guard symbols. The achievable channel block throughput with the optimum guard sequence is compared with that achievable with two common guard sequence types, namely zero stuffing and cyclic prefix.

This paper addresses the problem of soft equalization for space-time-coded transmissions over frequency-selective fading channels. The structure of the space-time code is embedded in the channel impulse response for efficient joint equalization and decoding. The proposed equalization/decoding approach uses a prefilter to concentrate the effective channel power in a small number of taps followed by a reduced-complexity maximum a posteriori probability (MAP) equalizer/decoder to produce soft decisions. The prefilter introduces residual intersymbol interference which degrades the performance of MAP when applied to the trellis of the shortened channel. However, the shape of the overall shortened channel impulse response allows the M-algorithm to approximate the prefiltered MAP performance with a small number of states. Based on this general framework, we investigate several enhancements such as using different prefilters for the forward and backward recursions, concatenating two trellis steps during decoding, and temporal oversampling. The performance is evaluated through simulations over the EDGE typical urban channel.

We consider turbo equalization of space-time-coded transmission over frequency-selective fading multiple-input-multiple-output (MIMO) channels. A MIMO finite-impulse-response prefilter is proposed and shown to reduce the turbo equalizer complexity significantly at a small performance loss. Advantages of the proposed scheme are that we do not alter the equalization algorithm or require the channel to be minimum phase. The prefiltered turbo equalizer is an attractive receiver structure for broadband wireless transmission using spectrally-efficient high-order modulation schemes as in EDGE.

This paper presents an overview of candidate equalization schemes for space-time-coded transmission over broadband wireless channels. Promising equalization schemes for space-time trellis and block codes are described and their pros and cons discussed. Performance results for the third-generation TDMA cellular standard EDGE are also presented.

Three space-time block-coding schemes with two transmit antennas for frequency-selective fading channels are described and compared. The three schemes implement the symbol-level Alamouti scheme (see Alamouti, S., IEEE Journal on Selected Areas in Communications, p.1451-8, 1998) developed for flat-fading channels at a block level either in the time or frequency domains. Receiver structures that aim at exploiting the spatial diversity offered by the two transmit antennas and the temporal diversity offered by the channel frequency selectivity are presented. The three schemes are applied to the EDGE TDMA system and their performance compared on a typical urban channel under both perfect and estimated channel conditions at the receiver.

Finite-length delay-optimized multi-input multi-output (MIMO) equalizers that optimally shorten the impulse response memory of frequency-selective MIMO channels are derived. The MIMO equalizers are designed to minimize the average energy of the error sequence between the equalized MIMO channel impulse response and an MIMO target impulse response (TIR) with shorter memory. Two criteria for optimizing the MIMO TIR are analyzed and compared. The presented analytical framework encompasses a multitude of previously-studied finite-length equalization techniques.

We show that the optimum length-/spl nu/ guard sequence for block transmission over a linear, noisy, and dispersive channel with memory /spl nu/ is a linear combination of the N information symbols of the block. The channel block throughput is maximized through a joint numerical optimization of N/spl nu/ linear combination coefficients determining the guard sequence and the N-dimensional auto-correlation matrix of the information symbols, subject to a total average energy constraint on the information and guard symbols. A closed-form solution for the optimum guard sequence is derived for the special case of high SNR. The achievable channel block throughput with the optimum guard sequence is compared with that achievable with two common guard sequence types, namely zero stuffing and cyclic prefix.

The effect of narrow-band interference (NBI) on the finite blocklength throughput of linear noisy dispersive channels is studied. Spectral characteristics (both shape and frequency location) of worst-case NBI and its associated minimum channel block throughput are derived. The performance of finite-length linear and decision feedback equalizers in the presence of worst-case NBI is presented.

We estimate bit error probability bounds for finite-length delay-optimised multi-input multi-output (MIMO) equalizers. These equalizers shorten the impulse response memory of frequency-selective MIMO channels by minimizing the average energy of the error sequence between the equalized MIMO channel impulse response and the target impulse response. We answer an important question in this paper namely, how much asymptotic loss in SNR do we expect as a result of this shortening? A partial distance spectrum for a 2/spl times/2 MIMO channel is evaluated with or without channel shortening equalisers. The union bound is then used to upper bound the bit error probability. Similarly, the lower bound is computed from the squared minimum Euclidean distance. Numerical results show that the expected loss is in the order of 2.5 dB for realistic wireless channel environments.

Transmitter optimization techniques for maximizing the throughput of linear ISI channels impaired by additive-Gaussian noise and crosstalk are presented. The transmitter ends of both single carrier and multicarrier transceiver structures are optimized subject to a fixed average input energy constraint. The effect of transmitter optimization on channel throughput is quantified by comparison with scenarios where both the desired user and the crosstalker use a flat energy distribution across the transmission bandwidth.

Optimum FIR transmit filters for symbol-by-symbol transmission on-linear dispersive additive-Gaussian-noise channels are derived by maximizing the channel throughput, subject to a fixed average input energy constraint. This maximized throughput is compared with that achievable with water-pour and flat transmit filters. The effect of transmit filter optimization on the receiver performance is investigated by considering the popular MMSE-DFE structure.

A time-varying structure and a time-invariant structure for the finite-length minimum-mean-square-error decision feedback equalizer (MMSE-DFE) are presented and their performances are compared on a stationary channel impaired by intersymbol interference (ISI) and additive Gaussian noise. The time-varying structure has an innovations error sequence but incurs a throughput loss on ISI channels because of its block-processing nature. Conditions under which the time-invariant structure exhibits near-optimal performance are described. Both structures converge to the canonical MMSE-DFE of Cioffi et al. (see ibid., vol.43, p.2582-94, 1995 and vol.43, p.2595-2604, 1995) as their filters' lengths become infinite.

Optimum finite-impulse response transmit filters for symbol-by-symbol transmission on linear dispersive additive Gaussian noise channels are derived by maximizing the channel throughput, subject to a fixed average input energy constraint. This maximized throughput is compared to that achievable with water-pour and flat transmit filters. The effect of transmit filter optimization on the receiver performance is investigated by considering the popular minimum mean-square error decision-feedback equalizer receiver structure.

A four-state adaptive maximum-likelihood sequence estimation (MLSE) Gaussian minimum-shift keying (GMSK) demodulator for modulation (BT=0.3) on AWGN channels is analyzed and simulated. This demodulator uses the linear representation of GMSK signals and achieves near-optimum BER performance. The channel-impulse response used in the MLSE demodulator is initialized to the highest energy component in the linear representation, and then adapted in a decision-directed mode to offset any performance losses incurred by initially ignoring other lower energy (and time-varying) components in the linear representation. The number of MLSE states is reduced to two, at about 0.1-dB performance loss, by implementing a whitening matched filter which concentrates most of the GMSK pulse energy in its two leading samples.

Mobile ground-based terminals observe significant Doppler on the forward channel when communicating through low Earth orbit (LEO) satellites. This paper deals with the analytic derivation of the Doppler shift measured by a user on the surface of Earth on a signal transmitted by a circular orbit LEO satellite. Two simplifications are performed to obtain the analytical expression of the Doppler shift as a function of time. First, during the visibility duration of the satellite at a terminal, the trajectory of the satellite with respect to the Earth is approximated by a great circle arc. Second, the angular velocity of the satellite with respect to the user is assumed to be constant. Numerical results validate the approximations. Another result of our analysis is an expression for the visibility window duration of a satellite at a terminal as a function of the maximum elevation angle. An algorithm for estimating the parameters of the Doppler curve based on a couple of Doppler and Doppler-rate measurements is also presented.

A tight lower-bound on the decision-point signal-to-noise ratio (SNR) of the infinite-length minimum mean square error decision feedback equalizer (MMSE-DFE) on additive white Gaussian noise (AWGN) plus near-end crosstalk (NEXT) impaired twisted-pair lines is derived in closed form. For the finite-length MMSE-DFE, the performance is completely determined by the maximum diagonal element in a Cholesky factorization of a structured correlation matrix. We illustrate how these results can be used for a quick performance evaluation and optimization of system design parameters.

A bandwidth-optimized and equalized multicarrier transceiver that achieves near-optimum performance at a practical complexity level is described. The equalizer used is a relatively short FIR filter whose taps and delay are set to optimize the performance of the multicarrier transceiver. Simulation results on a set of carrier-serving-area digital subscriber loops are also presented to demonstrate the separate and joint effects of bandwidth optimization and equalization on performance. Finally, the intriguing idea of using a pole-zero equalizer to achieve the high performance of long FIR equalizers at a much lower implementation cost is investigated.

The problem of approximating a long FIR filter by a reduced-parameter stable pole-zero filter is addressed. We derive a computationally efficient order-recursive algorithm that achieves this task with high accuracy. Our main emphasis is on applying this algorithm to reduce the implementation complexity of the decision feedback equalizer's long FIR feedforward and feedback filters encountered in high-speed data transmission on digital subscriber loops.

A new scheme for joint shortening of two long impulse responses using a single finite impulse response (FIR) equalizer is presented. The optimum settings, in the mean square error (MSE) sense, of the equalizer and the two (unit-tap constrained) shortened impulse responses are derived. The main application of interest is joint shortening of the channel and echo impulse responses for high-speed transmission on digital subscriber lines.

A new criterion for partially-equalizing severe ISI channels to reduce the cyclic prefix overhead of the discrete multitone (DMT) transceiver, assuming a fixed transmission bandwidth, is introduced. The equalized DMT is shown to recover a significant portion of the performance loss incurred because of the use of a moderate-size FFT in the DMT to reduce latency and implementation cost. In particular, equalizers designed using our new criterion result in a higher DMT performance margin than traditional mean-square-error DMT equalizers. Finally, additional promising methods that further enhance the performance of the equalized DMT are investigated.

Optimal transmit filters for packet-based data transmission on dispersive Gaussian-noise linear time-invariant channels are derived by maximizing the mutual information, subject to a fixed input power budget. A quasi-stationary approximation to the optimal nonstationary input covariance process is derived and shown to exhibit negligible mutual information loss from the optimal case, for situations of most practical interest. Moreover, this quasi-stationary approximation results in efficiently computed lattice or pole-zero implementations of the transmit filter. By considering the popular finite-impulse-response minimum-mean-square-error decision-feedback equalizer (FIR MMSE-DFE) as a receiver structure, we show that transmitter optimization results in an appreciable improvement in the decision-point signal-to-noise ratio. Finally, we show that, as the output blocklength becomes infinite, the optimum finite-dimensional nonstationary input covariance process converges to a stationary process whose power spectrum obeys the well-known water-pouring distribution.

A unified approach for computing the optimum settings of a length-N/sub f/ input-aided equalizer that minimizes the mean-square error between the equalized channel impulse response and a target impulse response of a given length N/sub b/ is presented. This approach offers more insight into the problem, easily accommodates correlation in the input and noise sequences, leads to significant computational savings, and allows us to analyze a variety of constraints on the target impulse response besides the standard unit-tap constraint. In particular, we show that imposing a unit-energy constraint results in a lower mean-square error at a comparable computational complexity. Furthermore, we show that, under the assumed constraint of finite-length filters, the relative delay between the equalizer and the target impulse response plays a crucial role in optimizing performance. We describe a new characterization of the optimum delay and show how to compute it. Finally, we derive reduced-parameter pole-zero models of the equalizer that achieve the high performance of a long all-zero equalizer at a much lower implementation cost.

A bandwidth-optimized and equalized multicarrier transceiver that achieves near-optimum performance at a practical complexity level is described. The equalizer used is a relatively short FIR filter whose taps and delay are set to optimize the performance of the multicarrier transceiver. Simulation results on a set of carrier-serving-area subscriber loops are also presented to demonstrate the separate and joint effects of bandwidth optimization and equalization on performance. Finally, the intriguing idea of using a pole-zero equalizer to achieve the high performance of infinite-complexity FIR equalizers at a much lower implementation cost is investigated.

The optimum set of modulation filters for multicarrier modulation on two-ray channels is derived in closed form. These filters are shown to be sinusoidal even for a finite number of carriers. The proposed modulation scheme optimizes the performance of two-ray channels corrupted by additive white Gaussian noise without incurring the data rate loss associated with FFT-based multicarrier transceivers. The case of multiple-ray channels is also considered.>

This paper extends a number of results on the infinite-length minimum-mean-square-error decision Feedback equalizer (MMSE-DFE) reported by Cioffi, Dudevoir, Eyuboglu and Forney (see IEEE Trans. Commun., 1995) to the finite-length case. Cholesky factorization and displacement structure theory are demonstrated to be two powerful analytical tools for analyzing the finite-length MMSE-DFE. Our objective throughout the paper is to establish finite-length analogs of the well-known infinite-length MMSE-DFE results. Similarities and differences between the two cases are examined and delineated. Finally, convergence of our derived finite-length results to their well-established infinite-length counterparts is shown.>

Computationally efficient algorithms are introduced for the real-time calculation of finite impulse response (FIR) equalizers for packet-based data transmission applications. It is found that a minimum mean-square-error decision feedback (MMSE-DFE) with arbitrary (finite) numbers of feedforward and feedback taps can be very efficiently computed from the channel response. The authors combine a recent theory of finite-spectral factorization for the MMSE-DFE with the theory of structured matrices to derive these efficient procedures for computing the equalizer settings. The method introduced is much more computationally efficient than direct computation by matrix inversion or than the use of popular gradient or least-squares algorithms over the duration of the packet.>