In this paper, we propose an aerial reconfigurable intelligent surface (ARIS)-enhanced unmanned aerial vehicle (UAV)-to-vehicle channel model that considers a three-dimensional (3D) continuously arbitrary trajectory. The model uses an ARIS mounted on a UAV to reflect signals to the terrestrial vehicle, which facilitates and enhances signal propagation. A generalized 3D random mobility model (RMM) with smooth turns is developed to characterize ARIS kinematics, where state differential equations and Euler approximation are employed for low-complexity real-time trajectory updates. Moreover, we model both the ARIS translational jitter and attitude jitter caused by wind or air turbulence as zero-mean Gaussian random variables, investigating the sensitivity of channel characteristics to assess the system robustness under non-ideal control conditions. The resulting trajectory and jitter models capture the unique channel properties in realistic scenarios. Furthermore, key statistical properties of the proposed channel model are derived, including spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs). We analyze the impact of ARIS trajectory and physical parameters on these statistical properties, such as velocity magnitude, phase shifts design, unit numbers, and rotation angles. Numerical simulation results demonstrate the superiority of ARIS-enhanced UAV communications using the proposed generalized 3D smooth-turn RMM with both translational jitter and attitude jitter, highlighting the significant role of ARIS in UAV communications.

In recent years, physical layer security (PLS) techniques have been paid considerable attention due to its high-security level and strong compatibility. However, the request of the superior legitimate channel is the Achilles’ Heel of PLS. A significant challenge exists in ensuring information confidentiality when the eavesdropper locates at user’s direction in the multi-antenna system. To overcome this limitation, we propose a novel framework to achieve secure communication via distributed split single sideband (SSB) time modulated array (TMA). By strategically dividing the I/Q paths of the transmitted signals into geographically separated subarrays, we establish the non-aliasing zone to achieve error-free communication for legitimate user (LU), while the eavesdropper positioned in the aliasing zone receives irrecoverably disturbed waveform. To assess performance, we adopt the encoder-decoder-based deep neural network to optimize the time-switching sequence, ensuring the subarrays’ spatial radiation areas overlap exclusively at the LU. A specialized loss function is formulated to enhance the interference-to-signal ratio by increasing the −1stto the +1stharmonic power ratio in non-LU regions. Furthermore, the joint optimization improves security by focusing energy on the LU while generating interference in non-LU areas. The bit error rate (BER) is used as the metric, and the simulation results validate the effectiveness of the proposed method, ensuring reliable transmission and increasing Eve’s BER to approximately 0.5, thereby compromising her ability to intercept the communication.

There has been increasing research attention on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings.

Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.

In this paper, an integrated sensing and communication (ISAC) system was proposed under cell-free architecture with different power allocation schemes for both communication and sensing functions. In addition to using uplink (UL) pilot transmission for channel estimation, a group of pre-designed orthogonal downlink (DL) pilot sequences are inserted and transmitted from each cooperative access point (AP) towards the user equipments (UEs). The inserted DL pilots in cell-free ISAC are utilized for both channel estimation in communication and cooperative localization in radar sensing. Based on the ISAC strategy, a power allocation scheme is proposed with the objective of minimizing the maximum channel estimation error for each user, under the power constraints of APs and minimum mean square error constraints of the localization. The full-power scheme which APs consume all the available power during the DL phase is also studied and analyzed. The performance of the proposed ISAC strategy is evaluated under a realistic highway scenario, and compared to the existing non-DL strategies. Additionally, we investigate the impact of the pilot ratio under a constrained signal length, as well as the impact of the pilot proportion that the pilot length to the entire signal frame length. Simulation results show that the performances of both channel estimation and localization can be significantly improved at a relatively low cost of extra power needed for APs. Moreover, the analysis provides insights into optimizing the DL/UL pilot ratio and pilot proportion which can significantly enhance both channel estimation and localization accuracy, while ensuring sufficient resources remain available for data payload.

Integrated sensing and communication (ISAC) is a key enabling technology for sixth-generation (6G) mobile communication systems, achieving seamless integration of communication and sensing functions. Cooperative sensing, where the transmitter and receiver are not co-located, serves as a key enabler for ISAC, significantly enhancing the sensing performance while reducing the implementation complexity of the receiver. However, practical deployments of cooperative sensing still face numerous challenges, such as synchronization and interference. This paper presents a set of advanced beam management methods specifically designed for the cooperative sensing system, offering a comprehensive framework to address these challenges. Specifically, we first analyze the strong/weak path effect (SWPE), a critical phenomenon caused by diverse target reflectivities and propagation paths, which severely degrades both synchronization accuracy and target detection. To counteract this, we propose an adaptive path power allocation method compatible with both all-digital and hybrid beamforming architectures. This method intelligently allocates power across different paths to mitigate the SWPE, thereby ensuring reliable synchronization via the direct path while enhancing the detectability of weak targets. As a result, the proposed method improves the target detection probability by over 30%. Furthermore, an adaptive interference suppression method is designed to reduce interference while maintaining sensing/communication quality, which obtains the SINR gain of around 5 dB, compared to the traditional full nulling method. Experimental results validate the effectiveness of robust synchronization and our proposed power allocation. This study lays a solid foundation for beamforming optimization in cooperative sensing systems, facilitating high-accuracy sensing and communication in complex environments.

In this paper, we consider a synthetic aperture secure beamforming approach for a virtual multiple-input multiple-output (MIMO) broadcast channel in the presence of hybrid wiretapping environments. Our goal is to design the flight node deployment constructed by a single-antenna mobile autonomous aerial vehicle (AAV), corresponding transmission symbol strategy, transmit precoding, and received beamforming to maximize the system channel capacity. Leveraging the synthetic aperture beamforming, we aim to provide spatial gain along a predefined angle in free space while reducing it in others and thus enhance physical layer (PHY) security. To this end, we analyze the expression of the asymptotic channel eigenvalues to optimize the AAV flight node deployment. For the optimal precoding design, an energy-efficient method that minimizes the transmit power consumption is studied based on the given virtual MIMO channel, while meeting the quality of service (QoS) for the base station (BS), leakage tolerance of eavesdroppers (Eves), and per-node power constraints. The power minimization problem is a non-convex program, which is then reformulated as a tractable form after some mathematical manipulations. Moreover, we design the received beamforming by applying the linearly constrained minimum variance (LCMV) method such that the jamming can be effectively suppressed. Numerical results demonstrate the superiority of the proposed method in promoting capacity.

This paper investigates near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems with sparse uniform planar arrays (UPAs). Based on the Green’s function-based channel model, the paper derives closed-form expressions for the signal beam power when the distance coordinate or the angular coordinates varies with respective to the focused position. Based on that, closed-form expressions for the lobe length and the suppressing ratio are obtained, indicating that both the distance-focusing property and the grating lobe behavior can be enhanced as the focal distance decreases or the antenna spacing increases. Furthermore, the paper introduces a crucial constraint on system parameters, under which effective degrees-of-freedom (EDoF) of XL-MIMO systems with sparse UPAs can be precisely estimated. Then, the paper proposes an algorithm to obtain a closed-form expression, which can estimate EDoF with high accuracy and low computational complexity. The numerical results verifies the correctness of the main results of this paper.

Leveraging inherent advantages of large bandwidth, high carrier frequency, and fine resolution, millimeter-wave (mmWave) technology is poised to play a pivotal role in integrated sensing and communication (ISAC) applications envisioned for sixth-generation (6G) networks. This paper proposes a stochastic geometry-based analytical framework to evaluate the performance of hybrid ISAC networks integrating sub-6 GHz and mmWave base stations (BSs). The framework explicitly incorporates band-specific propagation characteristics. Each mmWave BS is equipped with a large-scale antenna array to compensate for high-frequency propagation loss. Based on received signal power, we propose the maximum received echo signal power (Max-RESP) and maximum received average signal power (Max-RASP) association schemes to ensure that the target and user are associated with the BS providing better link conditions, respectively. Using stochastic geometry and probability theory, we derive analytical expressions for sensing distance accuracy and the communication achievable rate. The analytical results are validated via extensive Monte Carlo simulations. Numerical results show that hybrid sub-6 GHz and mmWave ISAC networks significantly outperform conventional pure sub-6 GHz networks and can approach the performance of pure mmWave networks by appropriately tuning the deployment density ratio. The appropriate density ratio provides practical guidance for balancing cost efficiency with performance enhancement. Moreover, the results reveal a performance bottleneck at higher density ratios, primarily due to the saturation of the signal-to-interference-plus-noise ratio (SINR). These findings highlight the crucial role of selecting an appropriate density ratio in hybrid sub-6GHz and mmWave ISAC networks.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

This paper investigates uplink transmission rate enhancement in space-air-ground integrated networks (SAGIN) by jointly designing precoders for a multi-antenna ground user and an unmanned aerial vehicle (UAV). Assuming a stationary relative position between the UAV and the satellite, we propose two joint precoding designs to optimize the uplink transmission rate while considering the practical finite signaling. We introduce an alternating iteration optimization approach (AIOA) when accurate channel state information (CSI) of the Rician link from the ground user and UAV is available. Additionally, we account for the statistical CSI induced by multi-path effects in various terrestrial environments, and derive a new closed-form expression for the uplink transmission rate. Building on this, a convex optimization framework is formulated by vectoring the optimization matrix and introducing auxiliary variables to tackle the non-convexity of the problem. Then, the AIOA is further adopted to jointly optimize the ground user and UAV precoders, significantly reducing computational complexity. Simulation results confirm the efficiency of the proposed AIOAs improving uplink transmission rates in SAGIN.

Cell-free radio access networks (CF-RANs) with network-assisted free-duplex (NA-FD) architecture unify flexible-duplex, hybrid-duplex, and full-duplex operations, but the coupled uplink/downlink interference and centralized processing impose substantial computational and signaling burdens. To address these challenges, this paper studies distributed transceiver design and access point (AP) duplex mode selection under edge distributed unit (EDU)-level information constraints and per- AP power limitations. We develop a partial distributed block coordinate descent (PDBCD) algorithm that decomposes the original sum-rate maximization into three tractable subproblems and iteratively approximates MMSE performance. The proposed design fully leverages EDU computing resources, reducing the computation load at the cloud computing unit (CCU), and significantly lowering fronthaul signaling overhead through an adaptive inter-EDU information sharing mechanism. Furthermore, by integrating a low-complexity greedy search for duplex mode assignment, the algorithm effectively mitigates cross-link interference (CLI) in NA-FD systems. Simulation results show that the proposed scheme improves spectral efficiency compared with conventional duplex mode, and centralized/distributed MMSE baselines, while maintaining strong scalability under practical deployment constraints.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), and there are inevitably discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) scalable CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may exceed far beyond the cyclic prefix (CP) duration. To address the issue, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and derive closed-form expressions of the spectral efficiency (SE) for downlink asynchronous transmission. Both scalable centralized and distributed implementations are taken into account. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association (BSUA) algorithms considering the impact of asynchronous timing offset. Simulation results demonstrate that asynchronous interference can severely degrade performance in wide-area scenarios, and our proposed PBTA scheme exploits beam-domain synchronization to align signal arrivals, effectively suppressing asynchronous interference and delivering notable performance gains. Additionally, the proposed BSUA algorithms achieve superior SE performance with low computational complexity.

In this paper, we propose a three-stage unified channel estimation strategy for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels, where the base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the developed three-stage strategy stems from the careful design of both the pilot signal sequence of the users and the vectors of RIS. Specifically, in Stage I, the cascaded channel components are eliminated by configuring the RIS phase shift vectors with a π difference to estimate the direct channels for all users. The orthogonal subspace projection is employed in Stage II to obtain equivalent signal matrices, enabling the estimation of angles of departure (AoDs) of the user-RIS channel for all users. In Stage III, we combine the signals of the time slots with the same pilots and project obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and remaining parameters of the cascaded channel for all users by exploiting the sparsity and correlation in the obtained equivalent matrices. Simulation results demonstrate that the proposed method yields better estimation performance than the existing methods.

In this paper, a precoding vector based index modulation (PV-IM) scheme is proposed. In the proposed scheme, precoding vectors are designed to maximize the Euclidean distance between precoded patterns, and their indices are used for information transmission. The proposed PV-IM scheme achieves superior performance than the constellation modulation scheme in high-rate scenarios, and overcomes the shortcomings of spatial index modulation (SIM) schemes. In addition, for the reconfigurable intelligent surface (RIS)-assisted multi-user (MU) scenario, a RIS-MU-PV-IM scheme is proposed based on the PV-IM scheme, where inter-user interference (IUI) is eliminated. The achievable rate, bit error rate (BER), and computational complexity of the proposed two schemes are analyzed. The impacts of channel estimation errors to the proposed two schemes are discussed. Extensive simulations are conducted to demonstrate the superiority of the proposed schemes.

Heterogeneous unmanned aerial vehicle (UAV) networks embedded with reconfigurable intelligent surfaces (RISs) present a promising paradigm for emergency wireless communications (EWC), offering enhanced coverage and resilience in harsh environments. However, extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts. Existing works lack a unified analytical framework for modeling CSI errors, employing inconsistent approaches that treat errors either as channel gain or as equivalent interference, leading to ambiguous benchmarks. To address this, we propose the $ζ$-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via $ζ$. This framework unifies the information-theoretic model (ITM) and the engineering baseline model (EBM) as the optimistic and pessimistic benchmark receiver treatments, while incorporating the simplified engineering model (SEM) as a tractable approximation. By employing the Fisher-Snedecor $\mathcal{F}$ distribution to capture severe fading and shadowing, we derive moment-matching-based closed-form or finite-sum approximate expressions and asymptotic expressions for average capacity (AC), effective capacity (EC), and outage probability (OP) under the proposed unified framework and its boundary cases. Validated by Monte Carlo simulations, our framework quantifies performance limits and provides crucial insights for designing robust and efficient EWC systems under various channel conditions and system impairments.

Satellite communications (SatCom) face fundamental security challenges due to their inherent broadcast nature. To address this, we exploit beyond-diagonal reconfigurable intelligent surface (BD-RIS) to unleash its full-scattering matrix control for enhanced secure beamforming flexibility in SatCom with rate-splitting multiple access (RSMA), where the satellite attempts to convey private signals to legitimate users with blocked direct downlinks and multiple eavesdroppers. To maximize the worst-case secrecy rate among legitimate users, a max-min fairness (MMF) problem is formulated with imperfect wiretap channel state information (CSI) via joint precoding, RIS configuration, and rate splitting optimization. By using the block coordinate descent (BCD) method, these optimization variables are decoupled with different subproblems and solved by the penalty dual decomposition (PDD) method iteratively. Furthermore, we develop a computationally efficient suboptimal solution that employs diagonal RIS (D-RIS) with reduced hardware and computational complexity, where alternating optimization (AO) and successive convex approximation (SCA) methods are employed to solve the non-convex problem. Simulation results demonstrate that our proposed BD-RIS-RSMA scheme achieves significant performance improvements compared to baseline schemes, while the suboptimal diagonal RIS scheme offers a favorable performance-complexity tradeoff.

The scalable cell-free radio access network (CF-RAN), built on cell-free massive multiple-input multiple-output (CF-mMIMO) networks, achieves a critical trade-off between computational complexity and system performance in cooperative transmission through the rational division of physical-layer functionalities. However, due to its distributed transmission architecture, unavoidable propagation delay differences arise in signal arrival times across different receivers during cooperative transmission, leading to asynchronous reception effects that severely degrade system performance. In this paper, we analyze the specific impacts of asynchronous reception effects in scalable CF-RAN systems, including accumulated phase offset on received signals, as well as inter-carrier interference (ICI) and intersymbol interference (ISI). We investigate channel estimation under non-ideal channel state information (CSI) acquisition caused by non-orthogonal pilot sequences and asynchronous reception effects, deriving closed-form expressions for the achievable uplink and downlink spectral efficiency (SE) in scalable CF-RAN systems under asynchronous conditions. To mitigate asynchronous reception effects, we introduce a beam division multiple access (BDMA) transmission scheme into scalable CF-RAN systems, leveraging large-scale antenna arrays at remote radio units (RRUs) to achieve beam-domain multi-user spatial multiplexing. Building on this framework, we implement per-beam time delay compensation (PBTDC) on RRU antenna arrays to approximate asynchronous received signals as synchronized and derive closed-form expressions for the achievable SE of uplink/downlink in asynchronous scalable CF-RAN systems with PBTDC architecture. Numerical simulations demonstrate that asynchronous reception effects significantly degrade channel estimation and data transmission performance in scalable CF-RAN systems. In contrast, the proposed PBTDC architecture based on BDMA transmission effectively mitigates these adverse effects and substantially enhances system performance.

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a successive convex approximation based deployment optimization method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

To realize the potential of integrated sensing and communication (ISAC) in cell-free (CF) massive MIMO system, an ISAC framework is proposed in this paper. With numbers and locations of scatterers (targets) unknown, based on received downlink data signals from the transmit access point (tAP) antenna, multiple receive access points (rAPs) first estimate delays locally. In each outer iteration, by comparing the estimated delays with the delays of the paths through extracted scatterer locations, selected rAPs search out mismatched estimated delays. Through cooperation, the potential locations of the scatterers forming each candidate location set corresponding to each mismatched delay are obtained, and each set will be evaluated in sequence. Specifically, a joint evaluation algorithm is proposed, where the probability model for the joint evaluation problem is established based on delay and limited angular information. Under the expectation maximization (EM) framework, by fusing information from all the rAPs, the extracted scatterer locations and candidate locations are adjusted, and the evaluation results are estimated, which can be regarded as the global probability of scatterers exist at candidate locations. When the evaluation results of all the locations in a candidate location set are low, the corresponding delay will be discarded, otherwise, the candidate location with the highest evaluation result in the set will be extracted. The proposed framework avoids exhaustive search in different associations of scatterers and estimated delays, and is more flexible than extraction from all the candidate locations based on fixed thresholds. Then, based on a simplified model, the impact of system parameters on delay based location sensing accuracy is revealed with the theoretical analysis. Finally, based on the prototype system with CF radio access network (RAN) architecture, the proposed framework is experimentally validated.

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the farfield case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network– Generative Pre-trained Transformer 2 (CNN–GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner. A pretraining–finetuning strategy is further adopted, where the model is first pretrained via masked prediction and then finetuned for the downstream beam prediction task, significantly improving training efficiency and accuracy. Simulation results under the 3GPP TR 38.901 channel model demonstrate that the proposed method achieves higher beam prediction accuracy than conventional recurrent models, while maintaining competitive performance in normalized beam-forming gain. These results confirm the feasibility of employing large-model AI for robust and low-overhead near-field beam management in future 6G systems.

This paper investigates the two-timescale design for access point (AP) mode selection and power allocation to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode selection and power allocation are adapted to the slowly-changing statistical CSI. Firstly, the minimum mean square error (MMSE) estimator is applied to estimate the channels between the APs and the channels between the APs and the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and maximum ratio combining (MRC) detector, and derive the closed-form expressions of the ergodic rate of the UEs and the sensing signal-to-interference-plus-noise-ratio (SINR). A non-convex mix integer optimization problem is formulated to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex mix integer optimization problem. Extensive simulation results demonstrate the analytical accuracy of the closed-form expressions and validate the convergence and effectiveness of the proposed AP mode selection and power allocation scheme.

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability, radar information rate and coverage probability for sensing. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising technology for sixth-generation mobile communication systems. Building upon conventional CF-mMIMO, the cell-free radio access network (CF-RAN) architecture distributes physical-layer functionalities among access points (APs), edge distributed units (EDUs), and user-centric distributed units (UCDUs), striking a balance between complexity and performance. However, prior studies on CF-RANs have primarily focused on the physical-layer, while the scalability of the medium access control (MAC) layer remains insufficiently explored. This paper proposes a novel hierarchical scalable CF-RAN architecture that fully exploits the functional potential of distributed UCDUs, enabling a comprehensive decentralized paradigm spanning from the physical-layer to the MAC-layer. Closed-form uplink spectral efficiency (SE) expressions are derived for maximum ratio (MR), distributed full-pilot zero-forcing (FZF), and distributed joint partial zero-forcing (JP-ZF) combining, explicitly accounting for imperfect channel state information and pilot contamination. The analytical insights reveal the improved scalability and how distributed processing and partial information availability affect system performance. To support large-scale deployments, we further develop a structured massive access scheme, including UCDU-EDU deployment, UE-UCDU association, distributed pilot assignment and AP-UE association, centralized refinement, and distributed power control. Simulation results verify the accuracy of the theoretical analysis and demonstrate the superior SE, favorable fairness, and enhanced scalability of the proposed schemes.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglected the inherent difficulty of channel estimation (CE) for RIS-assisted systems, and the discrete phase shift constraint in practical deployment. In this paper we design a new element-wise RIS architecture and propose a distributed location-aided transmission scheme with low complexity to enhance the reflected gain for channel state information (CSI)-limited RIS-assisted near-field communications. Specifically, the new element-wise RIS provides dynamic element selection capability with low hardware resources. Based on Fresnel diffraction theory, we construct the mapping from locations in space-domain to phase distributions of waves in phase-domain and reveal the priority of elements for harvesting and reflecting. Then, the distributed beamforming design with the phase of determine-then-align is proposed, where the estimation overhead reduction stems from exempted requirements of RIS-associated CE at base station (BS). The asymptotic analysis indicates that the proposed scheme can achieve the optimal gain with a fixed proportion of reflective elements when RIS is large, followed by simulations to verify its superiority to other protocols.

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.

In this paper, we investigate the channel estimation for mmWave multiple-input multiple-output-(MIMO) orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios. By leveraging the low-rank nature of mmWave channels and the multidimensional characteristics of MIMO-OFDM signals across space, time, and frequency, the received signals are structured as a fourth-order tensor that fits a low-rank CANDECOMP/PARAFAC (CP) model. We propose an estimation of signal parameters via rotational invariance techniques (ESPRIT)type decomposition-based method to solve the CP decomposition, which exploits the Vandermonde structure of the factor matrix. The channel parameters are then estimated from the factor matrices. Simulation results show that our method outperforms existing benchmarks.

Projection-type receivers have recently emerged as a promising approach for uplink integrated sensing and communications (ISAC) systems, facilitating simultaneous uplink signal detection and target sensing. However, the signal detection problem within projection-type receivers faces challenges due to the high dimensionality and rank-deficiency of the equivalent channel matrix. To address this rank-deficiency issue, we introduce two novel variations to reconfigure the equivalent channel matrix: the Projection-Tikhonov (PT) receiver and the Projection-Orthogonal Multiple Access (P-OMA) transceiver. The PT receiver is specifically designed to mitigate the impact of rank-deficiency through regularization, while the P-OMA transceiver utilizes the independent columns of the equivalent channel matrix to transmit a reduced number of communication symbols. For signal detection with the reconfigured channel matrix, we demonstrate that while the linear decoding algorithm can be effectively computed for various projection-type receivers, it yields poor performance. Given the high dimensionality of the equivalent channel matrix, we propose an efficient iterative algorithm based on the extreme point pursuit (EXPP) framework, using a low-complexity linear detector as the initial point. Finally, simulation results validate the effectiveness of the proposed design.

In this paper, a three-phase joint direct and cascaded channel estimation strategy is proposed for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multipleoutput (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels. The base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the devised three-phase strategy is contingent upon the meticulous design of the pilot signal sequence and the RIS phase shift vectors. Specifically, in Phase I, by reversing the RIS phase shift vectors, we remove the cascaded channel components to estimate the direct channels. In Phase II, we employ the orthogonal subspace projection to obtain equivalent signal matrices for the estimation of angles of departure (AoDs) of the user-RIS channel. In Phase III, we combine the signals of time slots with the same pilots and project the obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and the remaining parameters of the cascaded channels. Simulation results show that the proposed method outperforms existing methods.

In most existing works focusing on channel estimation for reconfigurable intelligent surface (RIS)-Aided MU-MIMO mmWave systems, Simultaneous Orthogonal Matching Pursuit (SOMP) method is used to estimate the angle of departure (AoD) at the users to convert the overall MIMO cascaded channel into multiple MISO cascaded channels for further estimation. However, it has poor performance when the number of antennas is small due to the strong correlation of atoms in the dictionary. This paper proposes a novel uplink two-phase channel estimation scheme with high accuracy in angle domain. Specifically, in the first phase, by carefully designing the precoding matrix, the 1-th sub-cascaded channel related to the 1 -th antenna is separated and estimated. In the second phase, by utilizing the invariance of angles and the linear correlation of gains, AoDs can be estimated based on the one-dimensional search method with high accuracy. Thus, all the remaining sub-cascaded channels can be calculated and combined into the overall MIMO cascaded channel. Simulation results demonstrate the superiority of the proposed algorithm.

Toward the sixth generation (6G) Internet of vehicles (IoV) networks, key challenges such as massive connectivity, high mobility and superior energy efficiency have driven the development of advanced wireless technologies. Native artificial intelligence (AI) is expected to support diverse vertical industries and offer numerous emerging AI services for 6G. However, how to efficiently process AI services and improve resource utilization while ensuring quality of service is still a challenging problem. In this paper, non-orthogonal multiple access (NOMA) is applied in the designed three-layer IoV network architecture. Then, an energy efficiency maximization problem is formulated by jointly optimizing NOMA transmission power and AI task deployment decisions. Third, a two-level iterative algorithm is proposed using the Dinkelbac’s method. Simulation results verify that our proposed algorithm outperforms benchmarks in terms of energy efficiency.

In this article, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input-multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width in the angular domain is characterized. We discover that this minimum width is related to the antenna size, the antenna number, and the target angle. Meanwhile, we present lower bounds of the ambiguity function in the Doppler and delay domains, and show that the impact of the antenna size on the radar performance in these two domains is very different from that in the angular domain. Moreover, the performance enhancement brought by MAs exhibits a certain tradeoff between the main lobe width and the side lobe peak levels. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method, and show that its performance is very close to the baseline. Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate that MAs can reduce the main lobe width and suppress the side lobe levels of the ambiguity function, thereby enhancing radar performance.

Deep learning-based automatic modulation recognition (AMR) is essential for enabling access authorization and spectrum management for interconnected devices and sensors in Internet of Things (IoT) systems. However, the open collection of data and the use of third-party training resources may introduce security vulnerabilities, especially backdoor attacks. Current research allows attackers to mislead receivers into misclassifying signals as a specific modulation type, but the fixed position of the trigger restricts its use in complex wireless networks. In this work, we propose a novel spatially distributed multi-target backdoor attack (SMBA) method. This method utilizes a trigger pattern to manipulate all types of input signals into multiple target modulation types by embedding the stealthy trigger at different spatial locations within the input signals. SMBA disseminates malicious samples across multiple target types specified by attackers, making it difficult for defenders to predict the target type into which the modulation signal embedded with the trigger is classified. This work reveals new security threats to AMR and provides important insights for developing defense technologies for IoT systems.

The modeling and analysis of large-scale stochastic MEC systems are of great significance in providing useful design guidelines for practical MEC networks. In most prior works, the users generally adopt the same strategy to select appropriate MEC access points (MAPs), where the fact that the available mobile computing services are different among the randomly distributed users is ignored. To this end, this paper proposes a user-centric cooperative offloading scheme to enable more flexible and efficient user task offloading. Specifically, each user can be served by one or two MAPs based on both the communication performance and computing performance. To evaluate the performance gains acquired from the proposed task offloading scheme, we first derive the service mode assignment probability, link distance distribution, and interference intensity to capture the network characteristics. Further, the moment and the meta distribution of the task transmission performance are analyzed. Based on the above results, we focus on the distribution of the computation workload to evaluate the edge computing service capability. From the analytical and simulation results, it is demonstrated that compared with the non-cooperative offloading scheme, the proposed task offloading scheme not only achieves more reliable and fair task offloading but also increases the computing service capacity.

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, cellular vehicle-to-everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultralow latency and high reliability in vehicle-to-vehicle (V2V) communication. This article proposes a method that integrates graph neural networks (GNNs) with deep reinforcement learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the graph sample and aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on vehicle-to-infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

Phased array (PA) has received considerable attention as a representative multiantenna technique due to its inherent advantages of superior directionality, spatial multiplexing capabilities, and robust anti-jamming characteristics. However, PA suffers from relatively high hardware complexity and power consumption. As a low-complexity array technology with excellent beamforming capability, time modulated array (TMA) has attracted much attention in recent years. In this article, we exploit a TMA for enhancing the Age of Information (AoI) of covert communication. Specifically, we first propose the transmitter structures and the corresponding beamforming methods for the TMA scheme and the PA scheme as a benchmark. Subsequently, the closed-form expressions of the Kullback-Leibler (KL) divergence is derived to serve as the quantitative measure of communication covertness under both schemes, based on which the average covert AoI (CAoI) is derived to jointly characterize the covertness and timeliness performance. Then, to minimize the average CAoI, the optimization problems of the block-length and beamforming parameters for both the TMA and PA schemes are formulated and solved. Finally, the numerical results are provided to show that the proposed TMA scheme surpasses the PA scheme in terms of both the convergence rate and the average CAoI.

To enhance the edge offloading capabilities of massive Internet of Things (IoT) devices with limited resources, a novel task offloading algorithm, namely, reduced target deep deterministic policy gradient (RT-DDPG), is proposed, which can generate near-optimal offloading decisions on the user and edge server sides, especially in mobile edge computing (MEC) and multiuser multiple input multiple output (MIMO) scenarios. In the RT-DDPG algorithm, the combination of Lyapunov optimization and improved deep deterministic policy gradient (DDPG) not only reduces the Q-value estimation bias of the neural network, but also constrains the long-term stability of the queue and reduces buffering delay. Moreover, by placing the algorithm agent independently on the device side, each device can adaptively formulate a decentralized computing offloading strategy based on environmental information. The simulation results show that with the help of the RT-DDPG algorithm, the optimal dynamic offloading strategy can be learned in the continuous action space. Compared with traditional reinforcement learning and other greedy strategy algorithms, the RT-DDPG algorithm can reduce the long-term average computing cost of users by 50%.

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collaborative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms.

Intelligent reflecting surface (IRS) based symbiotic radio (SR) technology offers a promising approach to enhance Internet of Things (IoT) connectivity. Despite its potential, current simple implementations face significant security challenges that could undermine the reliability of IoT connectivity. To tackle this security issue, this paper introduces a novel model for covert SR transmission that employs joint passive and active beamforming strategies to counteract risks from multiple warders. In this model, the IRS acts as a secondary transmitter, backscattering binary phase shift keying signals. The system is categorized into two operational modes: parasitic SR (PSR) and commensal SR (CSR), based on the synchronization requirements between primary and secondary signals. A comprehensive analysis of the bit error rate for each mode is conducted, and the Kullback-Leibler divergence is leveraged to measure system covertness, establishing a tractable covertness constraint for subsequent optimization. To address the effects of imperfect channel state information on beamforming performance, a robust joint beamforming algorithm is proposed. Our results show that this algorithm outperforms existing baselines. Particularly in CSR mode, high reliability and strong covertness can be achieved with a minimal number of IRS reflecting elements, providing a cost-effective solution for communication security in IoT applications.

A radio environment map (REM) is a visualization method that display electromagnetic properties, such as received signal strength, channel gain, and power spectrum density in combination with geographic information. The map can effectively support modern Internet of Things (IoT) network planning and resource management. A novel REM construction scheme of an arbitrary height and frequency in 3-D space is studied in this article. First, a complex urban environment is considered, where the radiation sources transmit wireless signals in different frequency bands. Then, the construction is sliced into 2-D planes with various elevations to achieve precise and efficient sensing of 3-D space. For near-ground scenarios, preliminary global interpolation based on linear unbiased estimation is first performed to obtain a coarse REM, and then graph neural networks are utilized to further extract the relationships and features of the spatial nodes to improve the construction accuracy. For high-altitude scenarios, a small range of interpolation is carried out on the basis of linear unbiased estimation with the clustering center obtained by clustering the known sensing nodes as the center of the circle. Then the global construction is implemented via domain transformation processing to increase the construction speed. Finally, the 2-D REMs are stacked in sheets according to elevation to form a 3-D REM. The simulation results demonstrate the effectiveness and superiority of the proposed scheme.

The rapid development of space-air–ground integrated technology has laid a foundation for achieving wide area wireless communication coverage, but its applicability is limited due to the limited computational capacity and energy storage of the terminal devices. This article thus proposes a space-air–ground integrated mobile-edge computing (MEC) task offloading and computing resource allocation (SIMOC) algorithm. First, a space-air–ground integrated MEC system is designed in which the terminal devices in the system can offload tasks to air- and space-based servers while performing energy harvesting. Second, an optimization objective of maximizing the task execution benefit minus the sum cost of task offloading and execution is established, for which the problem is transformed into a time-slot-based minimization problem of queue-stability minus revenue using Lyapunov optimization. Finally, the energy harvesting and task offloading subproblems of the queue-stability-minus-revenue problem are solved, respectively, to maximize the total revenue while ensuring device stability. Simulation results show that the SIMOC algorithm can reduce the all-task completion time by up to 98.16% compared with only local task execution algorithm in the absence of newly added tasks and shows good performance in handling newly added tasks. Meanwhile, the SIMOC algorithm has better performance compared to the particle swarm optimization algorithm.

Federated learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles’ local models instead of the local data. The gradients of vehicles’ local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error (QE), which further affects the model accuracy and training time. To do so, the total training time and QE become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this article, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.

As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate, and reliability of wireless communication systems. Starting from the scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) with distributed cooperative transceivers under the open RAN (ORAN) architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the distributed transceivers. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), and artificial intelligent-based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.

The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) System tailored for talking-face video communication. Firstly, we introduce a Generative Semantic Extractor (GSE) at the transmitter based on the FunASR model to convert semantically sparse talking-face videos into text with high information density. Secondly, we establish a private Knowledge Base (KB) based on the Large Language Model (LLM) for semantic disambiguation and correction, complemented by a joint knowledge base-semantic-channel coding scheme. Finally, at the receiver, we propose a Generative Semantic Reconstructor (GSR) that utilizes BERT-VITS2 and SadTalker models to transform text back into a high-QoE talking-face video matching the user’s timbre. Simulation results demonstrate the feasibility and effectiveness of the proposed LGM-TSC system.

As an evolution of cloud radio access network (C-RAN), fog radio access network (F-RAN) becomes promising for future mobile communications by enabling processing and caching at fog access points (FAPs). Different from the centralised C-RAN, F-RAN has a semi-distributed architecture, aiming to alleviate traffic load on the fronthaul links in C-RAN. Under the semi-distributed architecture in F-RAN, which employs a cell-free multiple input multiple output (MIMO) access technique, decisions on the joint user-FAP association and transmit power allocation are made at individual FAPs. To mitigate strong interference, FAPs will need to exchange cooperative status information, such as CSI, user association details or transmission power levels. However, this can lead to significant communication overhead within the network and introduce high complexity in the decision-making process. In this paper, accounting for the semi-distributed nature of the F-RAN architecture, reinforcement learning is leveraged as a potential solution to this kind of problem, and a novel multi-agent dual deep Q-network (MA-DDQN) algorithm is proposed by introducing experience exchange in partially observable Markov decision process environments. The simulation results show that the proposed reinforcement learning based algorithm outperforms the DDQN algorithm as well as the existing low-complexity algorithms.

Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existing schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.

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.

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems by enabling high fidelity exchange of semantic information at ultra-low rates. In this work, a generative SemCom framework utilizing pre-trained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. Using the rate-distortion-perception theory, the relationship between regenerated signal quality and transmission reliability is characterized, which is proven to be non-decreasing. Based on this, semantic values are defined to quantify the semantic similarity between multimodal semantic features and the original source. We also investigate semantic-aware power allocation problems that minimize power consumption for ultra-low rate and high fidelity SemComs. Two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Based on the Kodak dataset, perception-error functions and semantic values are obtained for image tasks. Simulation results show that the proposed semantic-aware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).

Life-transformative applications such as immersive extended reality are revolutionizing wireless communications and computer vision (CV). This paper presents a novel framework for importance-aware adaptive data transmissions, designed specifically for real-time CV applications where task-specific fidelity is critical. A novel importance-weighted mean square error (IMSE) metric is introduced as a task-oriented measure of reconstruction quality, considering sub-pixel-level importance (SP-I) and semantic segment-level importance (SS-I) models. To minimize IMSE under total power constraints, data-importance-aware waterfilling approaches are proposed to optimally allocate transmission power according to data importance and channel conditions, prioritizing sub-streams with high importance. Simulation results demonstrate that the proposed approaches significantly outperform margin-adaptive waterfilling and equal power allocation strategies. The data partitioning that combines both SP-I and SS-I models is shown to achieve the most significant improvements, with normalized IMSE gains exceeding 7 dB and 10 dB over the baselines at high SNRs (> 10 dB). These substantial gains highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

This paper proposes a novel channel estimation protocol for a reconfigurable intelligent surface (RIS) aided multi-user (MU) multi-input multi-output (MIMO) millimeter wave (mmWave) system under the hybrid architecture where the direct channels between the base station (BS) and user equipment (UE) exist. There are two stages respectively estimating the direct and cascaded channels. In Stage I, besides the direct channels, the angles of arrival (AoA) and the angles of departure (AoD) of the cascaded channels are also estimated. Stage II is divided into two sub-stages and the overall cascaded channels are estimated. In sub-stage I, the cascaded channel of a typical UE is estimated. In sub-stage II, the cascaded channels of all the remaining UEs are estimated. Simulation results demonstrate that the proposed method has lower pilot overhead and achieves higher accuracy than the existing benchmark approaches.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. Firstly, new near-field channel models and near-field XL-MIMO transmit beamforming (TBF) codebooks have to be adopted due to the dramatic increase in the number of antennas, which results in an excessive pilot overhead for beam training. Secondly, when the user density is high, the wireless propagation environments of the adjacent users are similar and hence the pilot signals received by the BS from different users appear to be interrelated, which is potentially beneficial but difficult to exploit. Thirdly, different users might share the same beam-direction, which causes excessive inter-user interference. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the best available near-field codeword for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme significantly improves beam training accuracy and reduces pilot overhead compared to traditional neural network-based benchmarks. Hence it is more suitable for multiuser XL-MIMO systems. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.

Combining millimetre-wave (mmWave) communications with an extremely large-scale antenna array (ELAA) presents a promising avenue for meeting the spectral efficiency demands of future sixth-generation (6G) mobile communications. This technology achieves a high data rate and establishes high-gain directional transmission links. However, beam training for mmWave ELAA systems is challenged by excessive pilot overheads as well as insufficient accuracy, as the huge near-field codebook has to be accounted for. In this paper, inspired by the similarity between far-field sub-6 GHz channels and near-field mmWave channels, we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the optimal near-field mmWave codeword, which aims to reduce pilot overhead and bypass the channel estimation. Moreover, we adopt deep learning to perform this dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and a novel neural network structure called NMBEnet is designed to enhance the precision of beam training. Specifically, when considering the orthogonal frequency division multiplexing (OFDM) communication scenarios with high user density, correlations arise both between signals from different users and between signals from different subcarriers. Accordingly, the convolutional neural network (CNN) module and graph neural network (GNN) module included in the proposed NMBEnet can leverage these two correlations to further enhance the precision of beam training. To better evaluate the performance of the proposed algorithm, we employ state-of-the-art system simulation software to obtain realistic channel data. Simulation results demonstrate the superior performance of the proposed strategy compared to the exhaustive search scheme and existing deep learning-based schemes.

This paper investigates the spatial bandwidth of line-of-sight (LoS) channels in extra-large MIMO (XL-MIMO) systems. For linear large-scale antenna arrays (LSAAs) with transceivers randomly positioned in 3D space, a simple but accurate closed-form expression is derived to characterize the local spatial bandwidth. Based on this analysis, we examine the properties of local spatial bandwidth and further derive expressions for the effective spatial bandwidth and the achievable degrees of freedom (i.e., theKnumber) for LSAAs. We also conduct case studies for both coplanar and non-coplanar transmitting and receiving arrays, providing more concise and intuitive expressions for local spatial bandwidth and achievable spatial degrees of freedom. Finally, the impact of array geometry on LoS XL-MIMO channel capacity is explored. When the transmitting and receiving arrays are coplanar and perpendicular to the line connecting their centers, the effective degree of freedom of the LoS channel is found to be approximately maximized. This orientation also maximizes the channel capacity in near-field high-SNR scenarios.

In this paper, we propose a three-stage high-accuracy uplink channel estimation scheme that utilizes super-resolution algorithms for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems. The proposed protocol enhances both estimation accuracy and pilot overhead efficiency. In Stage I, we derive the covariance matrix of the received signal and estimate the common angles-of-arrival (AoAs) at the base station (BS) using super-resolution algorithms. In Stage II, we construct an equivalent multi-snapshot received signal matrix to estimate the cascaded angles-of-departure (AoDs) at the RIS for a typical user. This takes advantage of the invariance of angle information across multiple channel coherence blocks while accounting for varying channel gains. To further reduce noise impact, we apply the minimum mean square error (MMSE) criterion. The full channel state information (CSI) of the typical user is then estimated using a combination of super-resolution algorithms for angle estimation and the least squares (LS) method for gain estimation. In Stage III, we reconstruct the common BS-RIS channel and use the results from Stages I and II to estimate the full CSI for other users, significantly reducing pilot overhead. Simulation results demonstrate that the proposed method outperforms the existing approaches in terms of both angle estimation accuracy and overall performance, while maintaining the same pilot overhead.

With the growing demand for virtual reality (VR) applications, mobile wireless networks should support the connections of a massive number of VR users. To support ultra-high data rates of multiple simultaneously transmitted VR streamings, we propose a mobile edge computing (MEC)-assisted rate splitting (RS) VR streaming transmission system. In the proposed system, RS technology exploits the shared interests of multiple VR users and MEC offloads the partial rendering tasks to achieve a better quality of experience (QoE) for VR users. Aiming to minimize the total weighted energy consumption, we formulate a joint communication and computing resource optimization problem while ensuring the required distortion and latency of VR users. To deal with the formulated intractable problem, we propose a joint rendering offloading and resource allocation algorithm that alternately solves the subproblems of quantization parameters selection, rendering offloading decision, transmit precoding design, rate allocation of RS transmission, and computing resource allocation. The simulation results demonstrate the effectiveness of the proposed algorithm in saving energy consumption. Specially, the performance of the proposed algorithm is 22.1% higher than that of the multicast-unicast scheme and can achieve 95.9% of the exhaustive search based algorithm.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

This paper studies a comprehensive framework for reconfigurable intelligent surface (RIS)-assisted integrated communication, sensing, and computation (ICSC) systems. To satisfy the critical need for low-latency sensing, we formulate a weighted latency minimization problem encompassing both multi-user equipment (UE) and simplified single-UE scenarios. To address the formulated non-convex problem in the multi-UE scenario, we decouple the original problem into two subproblems, where the computational and beamforming settings are optimized alternately. Specifically, for the computational settings, we derive a closed-form solution for the offloading volume and propose a low-complexity algorithm based on the bisection search method to optimize the edge computing resource allocation. Additionally, we employ two equivalent transformations to address the challenge posed by the non-convex sum-of-ratios form in the objective function (OF) of the subproblem related to active and passive beamforming. Several techniques are then combined to address these subproblems. To bridge the gap between theoretical assumptions and practical deployments, a robust design extension accounting for imperfect channel state information (CSI) is developed using statistical error modeling. Furthermore, a low-complexity algorithm that offers closed-form solutions is developed for the simplified single UE scenario. Finally, simulation results substantiate the effectiveness of the proposed framework.

Time-varying channels are significantly influenced by the Doppler effect, which leads to rapid changes in channel gain and requires Doppler frequency estimation to compensate for channel phase shifts and improve communication quality. In this paper, we propose a novel fifth-generation (5G) new radio (NR) orthogonal frequency division multiplexing (OFDM)-based transmission structure for time-varying channel estimation in high-mobility scenarios. By designing an appropriate subcarrier spacing and slot format, we ensure that the pilot signals remain nearly invariant within a single slot and exhibit rotational invariance between different slots. Leveraging this rotational invariance, we introduce a novel algorithm based on Vandermonde-structured tensor decomposition, which is non-iterative and has lower computational complexity and higher robustness than other tensor-based algorithms. Moreover, we provide a theoretical analysis of the uniqueness condition of tensor decomposition, proving that the proposed algorithm has strong feasibility and requires low pilot overhead. We also analyze the mean square errors (MSEs) of the parameter estimates and present a concise derivation of the Cramér-Rao Bound (CRB). The results demonstrate that the proposed algorithm significantly outperforms compressed sensing (CS)-based methods and other tensor-based methods in terms of parameter estimation performance at medium to high SNR. Furthermore, the proposed algorithm, based on the instantaneous channel model, offers higher channel estimation accuracy than the Kalman filtering-based algorithm, which relies on statistical channel models. Simulation results with channel data generated by Wireless InSite, which constructs a real-world scattering environment, demonstrate the high estimation accuracy of the proposed algorithm, validating its effectiveness in practical scenarios.

This paper presents a new multiple-input multiple-output (MIMO) communication system. The system, termed as “pseudo MIMO”, facilitates the transmission of more parallel data streams than the number of receiving radio frequency (RF) chains. The key principle involves connecting multiple antenna elements to each receiving RF chain and employing multiple analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling period. Through detailing the entire transmission and signal processing procedures by matrix manipulation, the equivalent channel matrix for each OFDM subcarrier is derived in a unified matrix form, which can be further represented by the product of the analog combining matrix and the frequency domain wireless channel matrix. As per the equivalent channel matrix, an optimization problem is formulated and solved to maximize the spectral efficiency by jointly designing the digital precoding and analog combining matrices. Numerical results demonstrate that the performance of a pseudo MIMO system can closely approach that of a fully digital (FD) MIMO system with the same antenna configuration but more RF chains. It is also revealed that a low-precision discrete analog combining scheme is sufficient to achieve nearly optimal performance. Further, the effectiveness of pseudo MIMO technology is validated through prototype testing.

To cope with the unendurable delay of maritime wireless networks (MWNs), this paper proposes a collaborative transmission framework utilizing a multi-antenna uncrewed surface vessel (USV) and multiple cache-aided buoys to satisfy the on-demand file requirements for remote users (RUs). Specifically, a direct transmission scheme is adopted for hit-requested files and a multi-hop transmission scheme is devised to handle cache misses. To fully exploit the local cache and signal processing capabilities, we integrate two schemes into a collaborative transmission framework, where the USV dynamically supports buoys in uncached file fetching, and buoys collaborate to forward both cached and fetched files to RUs through a cooperative beamforming policy. We aim to minimize the overall transmission completion time by jointly optimizing the USV trajectory, cooperative beamforming, and transmission duration under the constraints of USV kinetic, transmit power, and file requirements. By leveraging the completion condition analysis, the original problem is transformed into a sequence of one-slot problems and a finite-horizon problem, where the closed-form solution for the local caching beamforming at each buoy is derived. Due to the complexity of the multivariable coupling, we propose an equivalent rate transformation method for transmission strategy design. Numerical results validate the effectiveness of the proposed scheme and algorithm.

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a two-dimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-conditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first proposed. To further enhance security without requiring additional storage for new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Building upon these three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the strengths of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed specifically for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the proposed AM-SUEO-DBLTKM-RFID protocol achieves both low computational complexity and low storage overhead, making it well-suited for deployment in resource-constrained, low-cost RFID tags.

Covert communication is an important approach to ensure information security by hiding the transmission behavior. Space-domain-coding intelligent reflecting surface (SDC-IRS) can adjust the phase of the reflection signal for passive beamforming in angle domains, which is widely employed in covert communications. However, the gains by SDC-IRS vanish when the warder is proximal to the receiver in angle domains. To overcome this limitation, in this paper, the space-time-coding IRS (STC-IRS) is considered, which can adjust both the phase and the frequency of the reflection signal for passive beamforming in angle-distance domains. Specifically, system performance under STC-IRS and SDC-IRS is compared, revealing the essence that angle and distance discrimination for the receiver is achieved with STC-IRS. Further, to fully exploit STC-IRS, optimization problems are formulated to maximize the covert rate in both line-of-sight scenarios and Rician fading scenarios. To solve the above problems, penalty-based algorithms are proposed where the transmit power, the phase shift and the frequency shift at STC-IRS are optimized jointly with majorization-minimization and block successive upper bound minimization techniques. Considering more general and adverse cases, the proposed algorithms are also extended to the scenario with multiple warders. Simulation results demonstrate the superiority of the proposed scheme compared with other benchmarks. Especially, when the warder and the receiver overlap in angle domains, covert rates with STC-IRS exceed 3 bps by distance domain discrimination, whereas covert rates with SDC-IRS are less than 0.01 bps.

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, the investigation of the channel statistics based on the planar-wave-based model will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled unmanned aerial vehicle (UAV)-to-vehicle communication systems, which can be used to efficiently capture the sparse features of RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (FCFs), channel capacities, and path loss statistics with respect to the different physical features of the RIS array and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between the modeling complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled air-ground wireless networks.

Hostile jamming and eavesdropping threats bring severe challenges to reliable and secure communication demands of future networks. In light of the potentials of high-altitude platform (HAP) providing wide communication coverage with low cost and Ad-hoc network facilitating flexible access without support by hardware infrastructure, this paper proposes a multi-HAPs assisted air-terrestrial integrated Ad-hoc networks (HAIN) framework to defend against jamming and eavesdropping simultaneously. Specifically, the HAPs align the beamformer to the terrestrial users while nullifying the reception of eavesdropper. In addition, the Ad-hoc network enables cooperative anti-jamming transmission, where the cooperative users (CUs) provide communication assistance by forming anti-jamming coalition for blocked users (BUs). Building upon this framework, we aim to maximize the sum rate of BUs by jointly optimizing the beamforming and cooperative coalition formation with the imperfect channel state information (CSI). To handle the intractable problem, we first convert the imperfect CSI into the worst-case one, and then a sequential convex approximation combined with first order Taylor series expansion is proposed to optimize the beamforming. Furthermore, for the optimization of anti-jamming coalition formation, we reformulate it as the coalition formation game (CFG) and a partial best coalition preference order is put forward to enhance the sum rate of BUs. With the help of exact potential game (EPG), it’s proved that the CFG can converge to stable coalition formation by exploiting the proposed distributed anti-jamming coalition formation algorithm. Simulation results demonstrate that the proposed scheme has the superior secure transmission performance to benchmark schemes.

The Industrial Internet of Things (IIoT) is envisioned as the future paradigm for the next-generation industrial system. To provide low-latency and high-reliable communication for a large number of battery-limited industrial equipments, this paper proposes a cell-free network-assisted full-duplex (NAFD)-enabled short packet communications (SPC) scheme in IIoT networks, where access points (APs) operating in the uplink (UL) mode or downlink (DL) mode simultaneously serve UL sensors and DL actuators on the same frequency resource. Considering the UL-to-DL error propagation as well as the dynamic between latency, reliability and energy efficiency (EE), we take the effective EE as the performance metric and investigate the effective EE maximization problem by jointly optimizing UL power, DL beamforming matrices, data rates and duplex mode selection. The formulated problem is a mixed-integer fractional programming and decomposed into three subproblems. We develop the optimal and suboptimal algorithms for joint power control and beamforming design. A semi-closed-form solution is derived for the optimal data rates, and an efficient duplex selection algorithm is also proposed to find the computationally optimal duplex mode. Simulation results demonstrate the convergence of proposed algorithms, and the flexible cell-free NAFD scheme can achieve better EE performance than benchmark schemes such co-time co-frequency full duplex (CCFD), frequency division duplex (FDD) and greedy algorithm (GA) schemes.

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for low-altitude sensing scenario, aiming at estimating the parameters of the uncrewed aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs’ parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across the multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs’ parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to conventional techniques.

For an uncrewed aerial vehicle (UAV)-aided energy-harvesting non-orthogonal multiple access (EH-NOMA) network, where an untrusted far user (UFU) overhears the confidential transmissions from base station (BS) to the trusted near user (TNU), how to ensure the security of TNU and achieve the reliability of UFU becomes an open issue. The traditional artificial noise (TAN) scheme just guarantees the security of TNU without focusing on the reliability of UFU, while the non-artificial noise (NAN) scheme only ensures the reliability of UFU but sacrifices the security of TNU. Thus, the TAN and NAN schemes are unable to guarantee the fairness between TNU and UFU. To this end, we employ digital network coding to encrypt confidential signals with artificial noise signals, and the proposed artificial noise (PAN) scheme not only protects the confidential transmissions of TNU but also guarantees the reliability of UFU. We use security-reliability tradeoff (SRT) and interference-reliability tradeoff (IRT) to evaluate the performance of TNU and UFU, respectively. Additionally, we derive the exact and asymptotic expressions of outage probabilities for both TNU and UFU, and the intercept probability for TNU. Numerical results verify the accuracy of our theoretical results and demonstrate that: 1) the SRT and IRT performance of the PAN scheme is better than that of the TAN and NAN schemes; 2) the PAN scheme achieves better secrecy for TNU compared to the TAN scheme. Meanwhile, the reliability of UFU in the PAN scheme is almost the same as the NAN scheme, indicating the PAN scheme simultaneously realizes the security of TNU and the reliability of UFU and the fairness of NOMA users is achieved by the PAN scheme.

This work aims to tackle the labor-intensive and resource-consuming task of indoor positioning by proposing an efficient approach. The proposed approach involves the introduction of a semi-supervised learning (SSL) with a biased teacher (SSLB) algorithm, which effectively utilizes both labeled and un-labeled channel data. To reduce measurement expenses, unlabeled data is generated using an updated channel simulator (UCHS), and then weighted by adaptive confidence values to simplify the tuning of hyperparameters. Simulation results demonstrate that the proposed strategy achieves superior performance while minimizing measurement overhead and training expense compared to existing benchmarks, offering a valuable and practical solution for indoor positioning.

Exploiting the reconfigurable intelligent surface (RIS) to mitigate the severe path loss for millimeter-wave (mmWave) communications is a promising approach to meet the demand of ultra-high rates for future wireless communications, benefiting from its energy-efficient and cost-effective features. Especially, the near-field gain can be attained to improve the communication performance with its expanded aperture. However, achieving such superiority necessitates the acquisition of full channel state information (CSI) of RIS-associated channels, which poses a significant challenge in the near-field region, hindering the deployment of RIS. To solve this problem, in this paper we propose a location based scheme as an alternative to mitigate the large overhead of channel estimation for rotatable RIS-assisted near-field communications. In particular, we leverage the properties of Fresnel zone to construct the parametric equations of the Fresnel ellipsoids to characterize the propagation geometry. Then, in the presence of inevitable location uncertainty, we give the criterion of practical discrete phase shift design based on the derived parametric curve, resembling the minimum distance criterion for signal detection. Also, the rotation of RIS is exploited to mitigate the impact of location error. Such processing can further improve the performance of the proposed scheme, verified by analytical and numerical results.

Due to the users' overlapping channels and the open nature of the wireless medium, inter-user interference and malicious jamming attacks deteriorate the performance of unmanned aerial vehicle (UAV) communications. With this focus, this paper proposes a novel integration of dual polarization, rate-splitting multiple access (RSMA), and stacked reconfigurable intelligent surface (RIS) transceiver into UAV networks, thus simultaneously mitigating the inter-user interference and malicious interference by fully exploiting their potentials in the power, space, and polarization domains. Building upon this architectural framework, a generalized sum rate maximization problem is formulated under the jammer's imperfect angular channel state information and unknown cross-polarization discrimination. To efficiently tackle the challenges posed by the intractable non-convex design problem with both high-dimensional variables and the multiple QoS constraints, a low-complexity optimization framework is presented, where a discretization method combined with quadratic property, a reduced-majorization-minimization algorithm, and two computationally efficient algorithms using block successive upper-bound minimization are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify the superiority and validity of our proposed architecture and optimization framework over benchmarks.

In this paper, downlink (DL) pilot based integrated sensing and communication (ISAC) is explored in cell-free networks. To improve both accuracy of channel estimation and localization in the ISAC based cell free network, a group of pre-designed DL orthogonal pilot sequences are introduced in the proposed ISAC strategy for both channel estimation for communications and cooperative sensing with radars. A power allocation scheme is proposed with the objective of minimizing the maximum channel estimation error for each user, under the power constraints of APs and minimum mean square error of sensing based localization. The performance of the proposed ISAC strategy is evaluated under a realistic highway scenario, and compared to the conventional methods. The results show that the performances of both channel estimation and localization can be significantly improved at a relatively low cost of extra power needed for APs near users.

Mobile edge computing (MEC) and vehicle-to-everything (V2X) communications are two promising technics to support delay-sensitive applications for autonomous driving. In this paper, road side unit (RSU) and assistant vehicle offloading are jointly considered to minimize latency for vehicular tasks in an MEC-V2X network. The impact of Doppler spread caused by high moving speed is also considered. The offloading decision, transmit power, bandwidth, and computation resource allocations are solved by formulating them as a joint optimization problem. The simulation results show that the proposed scheme significantly reduced the task latency, compared to three traditional methods, where task either run locally, or at MEC server, or adaptively between MEC server and local.

This paper investigates the performance of simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with direct links. We apply the two-timescale scheme to design the base station (BS) beamforming and the phase shifts of the STAR-RIS. Specifically, we derive the closed-form expression of the average achievable rate. Based on the derived rate, we theoretically draw insights from comparing STAR-RIS and conventional RIS under the same condition. Then, we optimize the phase shifts of the STAR-RIS using accelerated gradient ascent algorithm. Finally, numerical results are provided to validate our theoretical insights that STAR-RIS outperforms the conventional RIS under some special cases.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as one of the key technologies for future 6G networks, which can further improve spectral efficiency by deploying far more antennas than conventional massive MIMO systems. However, beam training in multiuser XL-MIMO systems is challenging. To tackle this issue, we propose a graph neural network (GNN)-based beam training scheme for the multiuser XL-MIMO system, in which only the far-field wide beams need to be tested for each user. Specifically, the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user, where the information of the surrounding users can also be utilized by the GNN to further improve the accuracy of the beam training. Simulation results show that the performance of the proposed scheme can approach that of the exhaustive scheme but has more than a 93 % reduction in the pilot overhead.

Abstract A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network.

In this paper, to enhance the communication quality of cognitive unmanned aerial vehicle networks (CUAVNs), we investigate the intelligent reflecting surface (IRS)-assisted CUAVNs, where a leading UAV (LUAV) is deployed for sensing spectrum and a group of following UAVs (FUAVs) transmit data to LUAV with the aid of IRS. Our objective is to maximize the achievable throughput of CUAVNs by jointly optimizing sensing duration, IRS passive beamforming and LUAV’s three-dimensional (3D) location, where LUAV’s 3D location is restricted by IRS, FUAVs and primary user. For IRS-assisted single FUAV case, the intractable non-convex optimization problem is resolved into three subproblems, which are solved by utilizing the bisection search method, the closed-form expression of the optimal IRS phase shift matrix and the successive convex approximation method, respectively. Finally, an efficient alternating optimization algorithm is developed to obtain a high-quality suboptimal solution. By exploiting the solutions of single FUAV, we further propose the throughput weighted sum (TWS) algorithm to solve the intricate non-convex problem in IRS-assisted multiple FUAVs case. To further reduce the complexity of TWS, the low-complexity location weighted sum (LWS) algorithm is proposed. Numerical results show that compared to the scheme without IRS assistance, the achievable throughput increases about 102% with the proposed single FUAV scheme, and over 88% with the proposed TWS-based multiple FUAVs scheme. Moreover, the performance gap between the low-complexity LWS and the TWS is less than 7%.

Ultrareliable low-latency communication (URLLC) has been envisioned as the paradigm shift for wireless industrial automation in the coming industry 4.0. Despite the flexibility in conjunction with stringent requirements of latency and reliability, URLLC cannot guarantee the determinism demanded by industrial automation to provide an in-order and low-jitter delivery. Motivated by the evolvements in Rel-17 that strengthen the intrinsic determinism of 5G system, this article studies deterministic URLLC to achieve low-latency and bandwidth-saving scheduling of time-triggered traffic in Industrial Internet of Things (IIoT), instead of using time-sensitive networking (TSN) or hybrid TSN-URLLC. However, due to the queueing congestion and wireless links changes, it is challenging to achieve deterministic scheduling with bounded end-to-end (E2E) latency and low-loss probability. To ensure the determinism and schedulability of URLLC, offset s in gate control lists (GCLs) and radio resources are assigned under constraints of zero congestion, sequential arrival, bounded latency, and ultra reliability. To further improve the latency reduction and bandwidth saving of deterministic URLLC, we find the feasible transmission delays subject to the contradiction on monotonicity of the E2E latency and bandwidth with respect to the transmission delay, then propose a scheduling method to obtain the best solution of three cases by optimizing transmission delay, offset, bandwidth, and the number of subchannel. Simulation results validate the analysis and show the performance gain of the proposed method in three cases.

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 article investigates semantic communication in high-speed mobile Internet of Vehicles (IoV), focusing on spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We propose a semantic-aware spectrum-sharing (SSS) algorithm using deep reinforcement learning (DRL) with a soft actor-critic (SAC) approach. We start with semantic information extraction, redefining metrics for V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). We then apply the SAC algorithm to optimize decisions V2V and V2I spectrum-sharing decisions on semantic information. This optimization aims to maximize HSSE and enhance the success rate of effective semantic information transmission (SRS), including determining the optimal V2V and V2I sharing strategies, transmission power, and the length of transmitted semantic symbols. Experimental results show that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum-sharing algorithms and spectrum-sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

This paper presents a comprehensive framework for jointly analyzing the angle estimation error and designing a three-dimensional (3D) positioning algorithm for an Internet of Things (IoT) millimeter wave (mmWave) positioning system. Initially, the azimuth and elevation angles of arrival (AoAs) at the anchors are estimated by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. The angle estimation error is then analyzed in terms of probability density functions (PDF) by utilizing the properties of the 2D-DFT algorithm and employing challenging derivations and linear approximations. The analysis reveals that the resulting angle estimation error is non-Gaussian, distinguishing it from previous studies. Next, the complex expression of the PDF for the AoA estimation error is simplified using the first-order linear approximation of triangle functions. Subsequently, a complex expression for the variance is derived based on the obtained PDF. Specifically, the variance for the azimuth estimation error is integrated separately according to the different non-zero intervals of the obtained PDF. Additionally, the closed-form expressions of the variances are formulated using generalized hypergeometric series. Finally, the two-stage weighted least square (TSWLS) algorithm is employed to estimate the 3D position of the mobile user (MU) using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm the non-Gaussian nature of the derived angle estimation error and demonstrate the superiority of the proposed framework.

LoRa is widely used in low-power Internet of Things (IoT) applications due to its low-power consumption, high-connection density, and wide coverage. How to optimize the physical layer parameter configuration of LoRa edge nodes is a bottleneck issue that restricts network performance. To reduce the energy consumption of the distributed LoRa networks, a reinforcement learning strategy based on link prior knowledge is adopted, and the resource allocation of the edge node of the LoRa networks is transformed into a multiarmed bandit problem. On this basis, a dynamic parameter selection algorithm suitable for LoRa edge nodes is proposed, namely, the link-weight-EXP3 (LI-WEX) algorithm. By defining the weight factors of parameters and rewards for nodes, the energy consumption factor in decision-making is enhanced, and the policy space at the node is processed based on link knowledge, thereby improving energy efficiency by selecting the optimal combination of parameters. The simulation results show that the LI-WEX algorithm not only converges faster and is robust in different scenarios, but also can effectively reduce network energy consumption.

The combination of communication and sensing is envisioned as a novel feature in the forthcoming sixth-generation (6G) wireless communication. The conventional approach to the joint sensing and communication (JSAC) system is utilizing one base station (BS) as both a sensing transmitter and a sensing receiver, which is known as monostatic sensing. However, the resulting self-interference issue requires additional hardware promotion to achieve full-duplexing. To overcome this issue, in this paper, we focus on cooperative sensing where the transmitter and receivers are non-co-located, which includes the bistatic and multistatic sensing. Specifically, the system model of cooperative sensing based on mobile networks is established. To demonstrate the feasibility of cooperative sensing, the bistatic radar cross section (RCS) is provided. As for the sensing method, a refined orthogonal matching pursuit (R-OMP) method is proposed to estimate the channel parameters and data fusion is also provided to derive the objects’ positions and velocities. Considering the non-negligible interference in the cooperative JSAC networks, we also discuss interference management in this paper. Simulation results show that the proposed cooperative sensing system improves the position and velocity estimation accuracy by over 20% when compared with monostatic sensing. The preliminary experiment results also verify the feasibility of the proposed system.

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.

Reconfigurable intelligent surface (RIS)-assisted spatial index modulation (SIM) is a promising candidate for future wireless communication systems due to its high energy and spectrum efficiencies. When only one transmit antenna and one receive antenna are active in the RIS-assisted SIM, we develop a phase-alignment algorithm based on the signature constellation, which utilizes the signature constellation technology to achieve a good bit error rate (BER) performance with a low complexity. Furthermore, in the general scenario, we propose a novel optimization algorithm for RIS reflection coefficients, called maximum Euclidean distance-based low BER (MED-LBER) algorithm. The proposed algorithm aims to maximize the Euclidean distance between different received signals to improve the system performance. The achievable rate, BER performances, and computational complexities of the proposed algorithms are analyzed in detail. The impacts of imperfect channels on the performance of the proposed algorithms are studied. Extensive simulations are carried out to compare the performance of the proposed algorithms with existing algorithms, and demonstrate their superior BER performance.

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.

This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at a dual-function radar-communication (DFRC) base station (BS) and the reflecting coefficients at an active RIS subject to the quality of service (QoS) constraints of communication user equipments (UEs) and the transmit power constraints of active RIS and DFRC BS. To tackle the optimization problem, the majorization-minimization (MM) algorithm is applied to address the nonconvex radar SINR objective function, and the resulting quartic problem is solved by developing an semidefinite relaxation (SDR)-based approach. Moreover, we derive the scaling order of the radar SINR with a large number of reflecting elements. Next, the transmit power allocation problem and the deployment strategy of the active RIS are studied with a moderate number of reflecting elements. Finally, we validate the potential of the active RIS in ISAC systems compared to passive RIS. Additionally, we deliberate on several open problems that remain for future research.

Due to long-term reliability, wireless control systems (WCSs) have attracted significant interest recently. However, mission-critical control requires stringent ultra-reliability and low-latency communication (URLLC) with massive data delivery, which are major challenges for conventional wireless networks. This paper investigates downlink URLLC in WCS, where the semantic communication is adopted at the control center to extract task-oriented semantic information from original large-sized data. To efficiency, the control center utilizes the rate splitting policy to deliver semantic information through private messages, while the semantic knowledge is transmitted through one common message. We aim to maximize the weighted sum semantic information transmission rate by jointly optimizing the semantic information extraction, delivery duration, rate splitting, and transmit beamforming, subject to several practical constraints, including recovery accuracy, quality of service requirements, communication latency and computation delay. By the problem decomposition, two sub-problems are obtained, where the closed-form solution for the semantic information extraction is derived at each step. Due to the complexity of the multivariable coupling in the channel dispersion, we propose fractional transformation methods for rate splitting design. Numerical results confirm that the RSMA and semantic communication design can complement each other for multiplexing gains enhancement and latency reduction to achieve overloaded connections.

Reconfigurable intelligent surface (RIS), composed of nearly passive elements, is regarded as one of the potential paradigms to support multi-gigabit data in real-time. However, in traditional CSI (channel state information) driven frame, the training overhead of channel estimation greatly increases as the number of RIS elements increases to intelligently manipulate the reflected signals. To conveniently use the reflected signal without complex CSI feedback, in this paper we propose a position-aided phase configuration scheme based on the property of Fresnel zone. In particular, we design the impedance based discrete RIS elements with joint absorption mode and reflection mode considering the fabrication complexities, which integrated the property of the Fresnel zone to resist the impact of position error. Then, with joint absorption and 1-bit reflection mode elements, we develop the two-step position-aided ON/OFF states judgement (TPOSJ) scheme and the frame structure to control the ON/OFF state of RIS, followed by analyzing the impacts of mobility and position error on our proposed scheme. Also, we derive the Helmholtz-Kirchhoff integral theorem based power flow. Simulations show that the proposed scheme can manipulate the ON/OFF state intelligently without complex CSI, thus verifying the practical application of our proposed scheme.

This paper investigates the performance of a two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while the central processing unit (CPU) utilizes the maximum ratio combining (MRC) technology to perform fully centralized processing based on the instantaneous overall channel, which is the superposition of the direct and RIS-reflected channels. Firstly, we derive the closed-form approximate expression of the uplink achievable rate for arbitrary numbers of access point (AP) antennas and RIS reflecting elements, which can be used to obtain energy efficiency through the proposed total power model. Relying on the derived expressions, we theoretically analyze the impact of important system parameters on the rate and draw explicit insights into the benefits of RISs. Then, based on the rate expression under statistical CSI, we optimize the phase shifts of RISs by using the genetic algorithm (GA) to maximize the sum rate and minimum rate of users, respectively. Finally, the numerical results demonstrate the correctness of our expressions and the benefits of deploying large-size RISs into cell-free mMIMO systems. Also, we investigate the optimality and convergence behaviors of the GA to verify its effectiveness. To give a more beneficial analysis, we present numerical results to show the high energy efficiency of the system with the help of RISs. Besides, our results have revealed the benefits of distributed deployment of APs and RISs in the RIS-aided mMIMO system with cell-free networks.

In this paper, satellite-terrestrial assisted multi-tier computing networks (STMTCN) are proposed to satisfy the growing computation demands of user terminals (UTs) in next generation wireless networks. In the STMTCN, UT’s computation task can be processed at different computing entities and a multi-tier computation model named computing depth is proposed to better reflect the multi-tier computing process. Then, we formulate a weighted sum energy consumption minimization problem via jointly optimizing UT-satellite association, computing depth, multiple-input multiple-out (MIMO) precoding, and computation resource allocation. The non-convex optimization problem is decomposed into four subproblems, each of which is solved iteratively. Specifically, the UT-satellite association subproblem is solved by quadratic transform based fractional programming and Lagrangian dual method and a closed-form expression is obtained. The computing depth for local tier and the satellite tier is solved respectively with first-order Taylor expansion. Then, MIMO precoding subproblem for UT and satellite offloading is solved by quadratic transform and interior point method (IPM). Finally, the computation resource allocation for UT and satellite is obtained in a closed-form expression and the GW computation resource allocation is solved by using IPM. Simulation results show that the proposed STMTCN and algorithms can fulfill the UT’s computing demands with low energy consumption.

The reconfigurable intelligent surface (RIS) has drawn considerable attention for its ability to enhance the performance of not only the wireless communication but also the indoor localization with low-cost. This paper investigates the performance limits of the RIS-based near-field localization in the asynchronous scenario, and analyzes the impact of each part of the cascaded channel on the localization performance. The Fisher information matrix (FIM) and the position error bound (PEB) are derived. Besides, we also derive the equivalent Fisher information (EFI) for the position-related intermediate parameters. Enabled by the derived EFI, we verify that both the ranging and bearing information of the user can be obtained when the near-field model is considered for the RIS-User equipment (UE) part of the channel, while only the direction of the UE can be inferred in the far-field scenario. This result is well known in the scenario that the curvature of arrival (COA) is directly sensed by the traditional active large-scale array, and we prove that it still holds when the COA is sensed passively by the large RIS. For the base station (BS)-RIS part of the channel, we reveal that this part of the channel determines the type of the gain provided by the BS antenna array. Besides, in the single-carrier, single snapshot case, it requires both the BS-RIS and the RIS-UE part of the channel works in the near-field scenario to localize the UE. We also show that the well-known focusing control scheme for RIS, which maximizes the received SNR, is not always a good choice and may degrade the localization performance in the asynchronous scenario. The simulation results validate the analytic work. The impact of the focusing control scheme on the PEB performances under synchronous and asynchronous conditions is also investigated.

In this paper, to stabilize the users’ quality of service (QoS), the symbol detection mean squared error (MSE) constrained hybrid analog and digital beamforming is proposed in millimeter wave (mmWave) system, and the reconfigurable intelligent surface (RIS) is proposed to assist the mmWave system. The inner majorization-minimization (iMM) method is proposed to obtain analog transmitter, RIS and analog receivers, and the alternating direction method of multipliers (ADMM) method is proposed to obtain digital transmitter. The proposed iMM and ADMM methods are faster than the semidefinite relaxation (SDR) and interior point methods, respectively. In order to counter against the changing large-scale path loss, the iMM method is helped by channel normalization to reduce the computational complexity, while the ADMM method is helped by the adaptive parameterizations to robust against the changing large-scale path loss. Simulation results show that the computational times of the proposed method are much faster than other methods, the proposed method is robust against the changing large-scale path loss, and the user’s bit error rate (BER) is stable under small to medium channel estimation errors.

This work proposes a covert communication scheme in a cognitive radio network where a secondary transmitter (ST) transmits confidential information to a secondary receiver under the cover of jammers with homogeneous Poisson point process. Specifically, we first analyze the detection performance of the primary transmitter (PT) and Willie under collaboration and non-collaboration modes. We then derive the covert transmission outage probability under ST’s correct and incorrect decisions for whether PT transmits or not and obtain the expression for the effective covert rate (ECR). In order to maximize the ECR, we derive the optimal value of the time allocation ratio, based on which, we also derive the optimal value of ST’s transmit power subject to the covertness constraint and some power constraints. Our examination shows the non-collaboration mode outperforms the collaboration mode in terms of achieving a higher ECR, because the uncertainty of the PT’s transmission in the former one will cause confusion at Willie and lead to an increased detection error rate. In addition, the proposed scheme effectively increases the ECR when compared with the scheme without the jammer.

Low earth orbit (LEO) satellites are expected to play an important role in enhancing terrestrial Internet of Things. This paper focuses on the challenge of joint terminal activity detection (TAD) and channel estimation (CE) in grant-free non-orthogonal random access (GF-NORA)-enabled LEO communication networks. To leverage the dominant line-of-sight (LoS) path and the limited angular spread characteristic of terrestrial-satellite links, we propose a two-stage joint TAD and CE (TS-JDE) detection scheme aimed to achieve lower computational overhead. This scheme utilizes a high-accuracy algorithm for LoS path estimation and leverages the channel correlations for low-complexity NLoS paths estimation. Specifically, in LoS estimation stage, to mitigate the inaccuracies caused by Taylor expansion errors, we propose an enhanced message-passing algorithm, termed TaMP-LoS. This algorithm exploits the structured sparsity in the delay-Doppler domain and incorporates the Lagrange remainder to evaluate the expansion error, thereby enhancing estimation accuracy. In the NLoS estimation stage, we develop a sparse Bayesian learning-based algorithm, named SBL-NLoS, designed to estimate NLoS channels characterized by the limited residuals of delay and Doppler. Simulation results show that our proposed algorithm can achieve better detection performance than the existing approaches for GF-NORA-enabled LEO networks.

In this paper, a mobile edge computing (MEC) assisted multi-layer architecture is proposed to support the implementation of federated learning in Internet of Things (IoT) networks. In this architecture, when performing a federated learning based task, data samples can be partially offloaded to MEC servers and cloud server rather than only processing the task at the IoT devices. After collecting local model parameters from devices and MEC servers, cloud server makes an aggregation and broadcasts it back to all devices. An optimization problem is presented to minimize the total federated training latency by jointly optimizing decisions on data offloading ratio, computation resource allocation and bandwidth allocation. To solve the formulated NP hard problem, the optimization problem is converted into quadratically constrained quadratic program (QCQP) and an efficient algorithm is proposed based on semidefinite relaxation (SDR) method. Furthermore, the scenario with the constraint of indivisible tasks in devices is considered and an applicable algorithm is proposed to get effective offloading decisions. Simulation results show that the proposed solutions can get effective resource allocation strategy and the proposed multi-layer federated learning architecture outperforms the conventional federated learning scheme in terms of the learning latency performance.

Ultra-reliable and low-latency communications (URLLC) plays a critical role for the coming era of wireless industrial automation, which increases the flexibility without moderating the stringent requirements of latency and reliability. The task for URLLC is to deliver short packets from sensors to actuators via the central controller reliably and timely, during which large bandwidth is required for ensuring stringent quality-of-service (QoS) metrics. Due to the scarce spectrum resource shared by a large number of devices, and the dynamic channel changes over wireless links, it is challenging to achieve bandwidth-saving URLLC with one single method. Motivated by the observation that groups of devices working in close proximity to each other can form device-to-device (D2D) communications, this paper considers multi-connectivity (MC) together with other cross-layer methods including grant-free access, data replication, broadcasting, and processor-sharing server to minimize the total bandwidth under the QoS constraints of URLLC. With the cross-layer design, we fisrt derive the packet loss probability including the factors of collision due to the contention-based access scheme and decoding error due to the dynamic wireless channel, and hence the overall reliability of MC is provided. Then, we establish a framework to minimize the total bandwidth of MC, where the relationship of collision and packet loss probabilities, the monotonicity of collision and decoding error probabilities, and the relationship of the third type blocklength and size rule are proved to find the optimal resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the considered cross-layer design.

The terahertz-wave communications are sensitive to the propagation environment. Line-of-Sight (LOS) is considered crucial in maintaining reliable connections between transmitters and receivers, providing high quality of service and estimating the terminals’ locations accurately. Therefore, maximizing LOS availability improves performance and the localization process in the future systems. In this paper, we propose a general framework to investigate the impact of blockages’ characteristics on the probability of a terminal of interest provided with a number of LOS links simultaneously by taking into consideration the correlation among these links. A comparison among three scenarios, namely correlation, no correlation and two-dimensional is presented to validate the accuracy of these scenarios in different environment densities. The simulation results validate the accuracy of our analysis and show that considering different values of the base station (BS) number and height can improve the system performance. Maximizing the LOS availability can be achieved by considering a higher number of BSs in low and medium environment densities and by increasing the height of BSs for dense environments. Furthermore, unlike in the low environment densities, the correlation between links needs to be addressed carefully when studying LOS availability in dense environments in order to meet the future requirements.

In this paper, we consider an active reconfigurable intelligent surface (RIS) to assist the multiuser downlink transmission in the presence of practical hardware impairments (HWIs), including the HWIs at the transceivers and the phase noise at the active RIS. The active RIS is deployed to amplify the incident signals to alleviate the multiplicative fading effect, which is a limitation in the conventional passive RIS-aided wireless systems. We aim to maximize the sum rate through jointly designing the transmit beamforming at the base station (BS), the amplification factors and the phase shifts at the active RIS. To tackle this challenging optimization problem effectively, we decouple it into two tractable subproblems. Subsequently, each subproblem is transformed into a second order cone programming problem. The block coordinate descent framework is applied to tackle them, where the transmit beamforming and the reflection coefficients are alternately designed. In addition, another efficient algorithm is presented to reduce the computational complexity. Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method. Simulation results validate the effectiveness of our developed algorithms, and reveal that the HWIs significantly limit the system performance of active RIS-empowered wireless communications. Furthermore, the active RIS noticeably boosts the sum rate under the same total power budget, compared with the passive RIS.

In this paper, a novel system utilizing a hybrid relay-intelligent reflecting surface (HR-IRS) to boost the security performance of directional modulation (DM) is established. In particular, the malicious attacker works in full-duplex (FD) mode and it will eavesdrop on confidential message (CM) as well as send malicious jamming. To maximize the secrecy rate (SR), a joint problem of optimizing the receive beamforming, transmit beamforming, power allocation (PA) factor, and phase shift matrix (PSM) of HR-IRS is formulated. Since the optimization problem is un-convex and the variables are coupled with each other, we address this problem by iteratively optimizing these variables. First, the receive beamforming is designed based on the generalized Rayleigh-Ritz theorem. Then, the transmit beamforming and PA factor are optimized via Dinkelbach’s Transform and successive convex approximation methods. And for PSM, two strategies, called separate optimization of PSM (SO-PSM) and joint optimization of PSM (JO-PSM), are proposed. Thus, two iterative schemes are proposed accordingly, namely maximizing SR based on SO-PSM (Max-SR-SOP) and maximizing SR based on JO-PSM (Max-SR-JOP). The former has a better performance and the latter has a lower complexity. Simulation results show that given a sufficient power budget of HR-IRS, the proposed Max-SR-SOP and Max-SR-JOP can enable HR-IRS-aided DM network to obtain a higher SR than that aided by passive IRS.

As an emerging technology for wireless communication, the intelligent reconfigurable surface (IRS) is made up of numerous low-cost passive elements with reconfigurable parameters, which can reflect signals with a certain phase shift and build a programmable communication environment. To reduce the high hardware cost and energy consumption in spatial modulation (SM), an IRS-aided hybrid secure SM (SSM) system with a hybrid precoder is proposed in this paper, where an optimization problem is formulated to maximize the secrecy rate (SR) by jointly optimizing the beamforming at IRS and the hybrid precoding at transmitter. For the IRS beamforming, an alternating direction method of multipliers (IRS-ADMM) scheme is first proposed. To achieve a higher SR, an IRS beamforming scheme via semidefinite relaxation (IRS-SDR) is put forward. To reduce the high complexity of IRS-SDR, we design a block coordinate ascend-based IRS beamforming scheme (IRS-BCA) with a closed-form solution. As for the hybrid precoding, two methods, called a successive convex approximation method based on the approximate secrecy rate (ASR-SCA) and a gradient ascend method based on cut-off rate (COR-GA), are presented. Simulation results show that the proposed IRS-ADMM and IRS-SDR harvest substantial SR performance gains over IRS-BCA. In comparison to the IRS-ADMM and IRS-SDR, the proposed IRS-BCA is of the lowest complexity at the cost of performance loss. Regarding hybrid precoding, the proposed ASR-SCA outperforms COR-GA in the high transmit power region. According to the complexity and SR performance of six combination methods including each IRS beamforming method and each hybrid precoding method, we selected three combinations: IRS-BCA plus ASR-SCA, IRS-ADMM plus ASR-SCA and IRS-SDR plus COR-GA. Moreover, it is showed that the SR performance achieved by the three combination methods is significantly higher than those of IRS with random beamforming and without IRS.

The utilization of a large-scale antenna array has led to substantial performance improvements in anti-jamming communications. However, due to the practical constraints of hardware cost and power consumption, deploying such a large-scale antenna array at the user side is impractical. Inspired by the remarkable advantages of reconfigurable intelligent surfaces (RIS), we propose an active-passive cascaded RIS-aided receiver architecture that facilitates the cost- and energy-efficient deployment of a large-scale antenna array at the user side, while also providing additional degrees-of-freedom for effective beamforming design. Building upon this architectural framework and taking into account the practical imperfections in the angular channel state information (CSI), we formulate a worst-case achievable rate maximization problem for anti-jamming communications. To address the challenges posed by the intractable non-convex design problem, we present a low-complexity optimization framework that obtains semi-closed-form solutions. Specifically, we first develop a Pareto-dual scheme to handle the general power constraints in devising the optimal precoder for the base station. Subsequently, by introducing a novel anti-jamming criterion and employing the discretization method to transform the imperfect CSI of jammers into a robust form, we derive two jamming-nulling feasibility conditions and a unified unit-modulus zero-forcing scheme to determine the coefficients of the passive RIS. To strike a satisfactory balance between complexity and performance, we further design three computationally-efficient algorithms based on alternating majorization-minimization (AMM) and conventional/modified cyclic coordinate descent (C/M-CCD) methods to obtain the coefficients of the active RIS. Finally, through comprehensive numerical simulations, we validate the effectiveness of the proposed architecture and optimization framework, demonstrating their capacity to achieve exceptional performance in a cost-effective manner.

Short-packet communications are applied to various scenarios where transmission covertness and reliability are crucial due to the open wireless medium and finite blocklength. Nonetheless, transmission covertness and reliability present a dilemma and significantly limit the throughput for short-packet communications. Intelligent reflection surface (IRS) is utilized in this paper to address this issue by enabling channel reconstruction, thereby releasing the fundamental limit between transmission covertness, reliability, latency (blocklength), and throughput. Specifically, to reveal the benefits brought by IRS, the achievable throughput without IRS is derived first in terms of the transmission covertness requirement, reliability requirement, and blocklength. Subsequently, the achievable throughput with IRS is derived, which releases the fundamental limit among the above-mentioned performance metrics. Besides, considering the phase uncertainty at IRS and limited channel estimation overhead, the cases where the warder/IRS knows instantaneous/statistical channel state information (CSI) of warder-related/legitimate links are all discussed, elucidating the impacts of CSI availability on the throughput. Furthermore, numerical results verify the accuracy of analytical results and demonstrate the benefits provided by IRS. Especially, the achievable covertness and reliability performance can be enhanced while the minimum required blocklength can be reduced by introducing IRS, which is crucial for short-packet communications.

Wireless short-packet communications pose challenges to the reliability and security of the transmission. Besides, the proactive warder compounds these challenges, who detects and interferes with the potential transmission. Thus, the tradeoff among reliability, covertness, latency (blocklength) and transmission rate is crucial, and efficient system design schemes are required for short-packet communications against the proactive warder. To address these issues, we investigate the reliable and covert performance of above systems. Specifically, detection error probabilities and their approximations are derived for cases where the warder knows the instantaneous/statistical channel state information. Besides, the decoding error probability is derived. The asymptotic relationship among the detection/decoding error probability, blocklength and transmission rate is established, revealing the non-trivial tradeoff between reliability and covertness performance. Furthermore, to maximize the effective throughput, an optimization framework is proposed under reliability and covertness constraints. Numerical results verify the tightness of the approximations and the feasibility of the optimization framework. Furthermore, it is shown that the proactive warder severely degrades the throughput compared to the passive one. And longer blocklength is always beneficial to improve the throughput for systems with optimized transmission rates, whereas the optimal blocklength is not necessarily the maximum one when transmission rates are fixed.

In this paper, the covert communication is considered with a full-duplex (FD) receiver under covertness and reliability constraints in the finite blocklength regime. Conventional covert communication systems are restricted by the square root law. To break through this limitation, an FD receiver is introduced, generating artificial noise (AN) to confuse the warder’s detection and achieving positive covert rates. To investigate the tradeoff among covertness, reliability, blocklength and throughput, the expressions of the warder’s detection error probability and the receiver’s decoding error probability in the finite blocklength regime are derived. Moreover, the asymptotic throughput is analyzed when the maximum transmit power of the FD receiver approaches infinity. Through the asymptotic analysis, we find that the blocklength’s impact on the covertness performance vanishes, which is a reason for achieving positive covert rates. However, the AN will also cause harmful residual self-interference to the receiver for which the FD receiver should be carefully designed by making a tradeoff between the covertness and the effective throughput. Hence, an optimization problem is formulated and solved to maximize the covert throughput. Numerical results show that the proposed scheme is effective in the finite blocklength regime where positive covert rates can be obtained.

This paper proposes to leverage intelligent reflecting surface (IRS) to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT) for an integrated sensing, communication and computation system. In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick response (QR) code, rather than a familiar reflection device. Meanwhile, a dual-functional radar-communication base station (BS) is used as a scanner to collect and recognize the information on each IRS carrying the data of its associated user equipment, while detecting the radar target. Based on the established system model, partial and binary data offloading strategies are respectively considered. By defining a performance metric named weighted throughput capacity (WTC), two maximization problems of WTC are formulated. According to the coupling degree of optimization variables in the objective function and the constraints, each optimization problem is firstly decomposed into two subproblems. Then, the methods of linear programming, fractional programming, integer programming and alternative optimization are developed to solve the subproblems. The simulation results demonstrate the achievable WTC of the considered system, thereby validating RFCF-UT.

In this paper, we investigate the energy minimization problem in cell-free mobile edge computing (MEC) networks under dynamic channel conditions and random arrival tasks. We aim to minimize the total energy consumption of user equipment (UEs) and MEC servers (MECSs) by jointly optimizing MECS active/sleep mode selection, UE-MECS association decision (MSAD), and task offloading, communication, and computation resource allocation (TORA) across two different timescales. Considering that the involved optimization variables affect the system performance in different timescales, we decouple the formulated stochastic optimization problem into two subproblems: MSAD operating in the large timescale and TORA in the small timescale. Leveraging the Lyapunov method, the stochastic TORA problem is decoupled into a series of deterministic problems, of which the closed-form solutions are presented. Furthermore, the MSAD problem is reformulated as a constrained Markov decision process (MDP). Then, we propose a double dueling deep Q-network (D3QN) to learn the optimal MSAD based on the TORA results. Numerical results demonstrate that the proposed online TORA-assisted MSAD learning algorithm has effective convergence and achieves substantial energy reductions for the MEC networks compared with the benchmark schemes.

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual channel state information (CSI) of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization–minimization (MM) algorithm framework, and, in each iteration low-complexity solutions for the BS and RIS beamforming are found based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

Large-scale reconfigurable intelligent surface (RIS) can effectively enhance the performance of millimeter wave (mmWave) unmanned aerial vehicle (UAV) to ground communication link with obstructed line-of-sight (LoS) path by exploiting more reflecting units. However, the non-negligible array dimension of large-scale RIS and the mobile property of the terminals bring significant variations in propagation char-acteristics, making conventional channel models inapplicable. To address this issue, we propose a dynamic sub-array partition scheme to divide the large-scale RIS into sub-arrays by exploiting the Rayleigh distance criterion and the mobile property of the transceivers. Based on the proposed scheme, a novel non-stationary channel model for large-scale RIS auxiliary mmWave UAV-to-ground mobile networks is developed, which outperforms existing models with well balance between model complexity and accuracy. Numerical results are provided to verify our analysis.

In this paper, we consider optimizing a multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) transceiver at the roadside unit (RSU) to detect a potential eavesdropping target and transmit the private information securely to the legitimate vehicular users. An optimization problem is formulated by optimizing the sum secrecy throughput of vehicle-to-infrastructure (V2I) links under requirements of waveform similarity and target return signal-to-interference-plus-noise ratio (SINR) threshold. To handle the challenging issue, we first cast the resulting non-convex problem into an equivalent optimization problem relying on the mean-square error (MSE) technique specified resource budget, and then develop an alternating procedure to decouple two optimization variables and decompose the resulting problem as two subproblems. To deal with the subproblem, we propose a dual ascent approach (DAA) based on the Limited-memory Broyden Fletcher Goldfarb and Shanno (LBFGS). Simulation results confirm the efficiency of the devised optimization method.

The integrated sensing and communications (ISAC) system, which integrates radar sensing and communication into the same hardware platform, has been considered as one of the new paradigms for the sixth-generation wireless networks. In order to significantly increase the communication rate and enhance the sensing accuracy with low cost and power consumption, in this paper the hybrid beamforming for millimeter wave (mmWave) ISAC systems is proposed. The hybrid analog and digital beamforming is jointly optimized to maximize the weighted sum rate (WSR) of communication users while satisfying the sensing target angle Cramér-Rao bound (CRB) constraint and transmit power budget. Based on the weighted minimum mean square error (WMMSE), Riemann conjugate gradient under exact penalty method (EPM-RCG), and successive convex approximation (SCA) methods, an efficient alternating algorithm is developed to optimize the WSR of communication users in the ISAC systems. Numerical results demonstrate the effectiveness of the developed hybrid beamforming for WSR maximization in mmWave ISAC systems.

The use of a large-scale antenna array has achieved significant performance gains in anti-jamming communications. However, due to the hardware cost and power consumption constraints, it is impractical to deploy such large-scale antenna array at the user side. Inspired by the remarkable advantages of reconfigurable intelligent surface (RIS), we propose an active-passive cascaded RIS-aided receiver architecture, which facilitate the deployment of a large-scale antenna array at the user side in a cost- and energy-efficient way and provides additional degree-of-freedom for beamforming design. Building upon this architecture and considering the practical angular channel state information (CSI) imperfection, a worst-case achievable rate maximization problem is formulated for anti-jamming communications. To handle the non-convex problem, a low-complexity optimization framework is proposed, where the new anti-jamming criterion, Pareto-dual scheme, unified unit-modulus zero-forcing scheme, and conventional-cyclic coordinate descent algorithm are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify that the proposed architecture and optimization framework are capable of achieving excellent performance with low complexity.

Vehicular networks enable vehicles support some real-time applications through training data. Due to the limited computing capability of vehicles, vehicles usually transmit data to a road side unit (RSU) deployed along the road to process data collaboratively. However, vehicles are usually reluctant to share data with each other due to the inevitable data privacy. For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model through aggregation, thus the data privacy can be protected through sharing model instead of raw data. The traditional FL requires to update the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload local models to update the global model. However, vehicles may usually drive out of the coverage of the marked RSU before they obtain their local models through training, which reduces the accuracy of the global model. In this paper, a mobility-aware vehicular asynchronous federated learning (AFL) is proposed to solve this problem, where the RSU updates the global model once it receives a local model from a vehicle where the mobility of vehicles, amount of data and computing capability are taken into account. Simulation experiments validate that our scheme outperforms the conventional AFL scheme.

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate the weighted throughput (WPT) maximization for cell-free (CF) multiuser (MU) MIMO multicarrier (MC) systems through resource allocation (RA) in finite blocklength regime (FBL) while ensuring the quality of service (QoS) of each user under the constraints of total power consumption. Since the channels vary in different subcarriers and inter-user interference strengths, the WPT can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design (BF). With this motivation, we propose a joint user scheduling (US) and BF algorithm to address an mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA scheme outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency (SE) than the centralized antenna systems (CAS).

Abstract This paper focuses on a reconfigurable intelligent surface (RIS)‐aided full‐duplex multi‐user massive multiple‐input multiple‐output system with transceiver hardware impairments (THWIs). Different from the existing works, the two‐timescale design scheme is considered. The phase shift at the RIS is designed only based on the statistical channel state information. Firstly, the closed‐form expression of the uplink and downlink achievable rate is derived. Then, the power scaling laws are revealed. Finally, the impact of THWIs on the system performance is analysed and genetic algorithm to maximize the achievable rate is used.

Downlink coherent multiuser transmission is an essential technique for cell-free massive multiple-input multiple-output (MIMO) systems, and the availability of channel state information (CSI) at the transmitter is a basic requirement. To avoid CSI feedback in a time-division duplex system, the uplink channel parameters should be calibrated to obtain the downlink CSI due to the radio frequency circuit mismatch of the transceiver. In this paper, a design of a reference signal for over-the-air reciprocity calibration is proposed. The frequency domain generated reference signals can make full use of the flexible frame structure of the fifth-generation (5G) new radio, which can be completely transparent to commercial off-the-shelf (COTS) remote radio units (RRU) and commercial user equipments. To further obtain the calibration of multiple RRUs, an interleaved RRU grouping with a genetic algorithm is proposed, and an averaged Argos calibration algorithm is also presented. We develop a cell-free massive MIMO prototype system with COTS RRUs, demonstrate the statistical characteristics of the calibration error and the effectiveness of the calibration algorithm, and evaluate the impact of the calibration delay on the different cooperative transmission schemes.

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.

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and$500 \mu \text{s}$latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

The use of a reconfigurable intelligent surface (RIS) in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector. To address this issue, we propose a RIS-aided amplify-and-forward (AF) relay network in this paper. By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS, two schemes are put forward to address a maximizing signal-to-noise ratio (SNR) problem. First, aiming at achieving a high rate, a high-performance alternating optimization (AO) method based on Charnes–Cooper transformation and semidefinite programming (CCT-SDP) is proposed, where the optimization problem is decomposed into three subproblems solved using CCT-SDP, and rank-one solutions can be recovered using Gaussian randomization. However, the optimization variables in the CCT-SDP method are matrices, leading to extremely high complexity. To reduce the complexity, a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation (DT-SCA) is proposed, where the variables are represented in vector form, and the three decoupling subproblems are solved using DT-SCA. Simulation results verify that compared to three benchmarks (i.e., a RIS-assisted AF relay network with random phase, an AF relay network without RIS, and a RIS-aided network without AF relay), the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance. Furthermore, it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss. However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network’s output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

Due to the flexible deployment, unmanned surface vessels (USVs) have attracted much interest recently. To solve the resource scarcity problem at sea, USV needs to leverage existing terrestrial and satellite systems for efficient backhaul and spectrum sharing. In this case, the multiple input multiple output (MIMO) technology can be applied for diversity gain improvement and interference coordination. However, how to adopt MIMO technology into maritime networks with a sparse scattering environment is still an open issue. In this paper, we employ a multi-antenna USV to support on-demand communications. Utilizing the two-ray channel, we aim to maximize the sum throughput over all USV intended users, by jointly optimizing the cooperative beamforming and trajectory, subject to several practical constraints, including the USV kinetics, quality of service requirement and backhaul capacity. Different from existing whole period designs, we decompose the problem into sequential one-slot problems. Within each slot, the non-convex problem is solved iteratively by using problem decomposition and successive convex optimization methods. Then, channel estimation errors are considered to investigate a robust beamforming scheme. Numerical simulations validate that the USV coexists well with the satellite network and show that the beamforming scheme and trajectory design complement each other for performance improvement.

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.

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

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network framework, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting coefficient by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.

The low hardware cost makes ultra-large (XL) reconfigurable intelligent surfaces (RIS) an attractive solution for enabling the intelligent electromagnetic environment, but it brings the challenge of near-field propagation channels. In this paper, we consider the propagation feature of the spherical wavefront in the near field of the millimeter-wave/sub Terahertz (mmWave/subTHz) localization system with the assistance of a RIS. The localization problem is investigated based on the derived second-order Fresnel approximation of the near-field channel model. In addition, the RIS training phase shifts and pilots are carefully designed to increase the channel rank so that the channel covariance matrix can be efficiently estimated. Simulation results validate the proposed near-field channel approximation and the localization algorithm.

Regionalized communication and computation architecture aggregates idle vehicular fog computation resources in urban cities without dedicated infrastructure deployment. Vehicular fog computing migrates computation services close to the edge of the network, which can break the long response latency limitation of cloud computing. However, the high mobility and random distribution of vehicles lead to the difficulty of the fog infrastructure deployment and the offloading decision. In this paper, we investigate the latency minimization problem in the regionalized three-tier vehicular fog network, which is divided into the sub-problems of single-region offloading decision and multi-region resource allocation. In particular, we make the preliminary offloading decision for users in each roadside unit (RSU) coverage area to solve the single-region offloading decision problem. We also transfer the multi-region resource allocation problem into a group-knapsack problem to obtain the optimal resource allocation results among all the users in the base station coverage area. Simulation results validate the superiority of our proposed algorithm, which enables latency reduction in the regionalized three-tier vehicular fog network.

The reconfigurable intelligent surface (RIS), com-posed of nearly-passive elements to induce reconfigurable re-flecting replicas of signal, is regarded as one of the promising approaches to support multi-gigabit data about the surrounding environment and recognizing objects in real-time. This makes RIS a potential technology in the autonomous vehicle application. However, the application of RIS and the mobility of vehicles substantially increase the training overhead of channel estimation, without which the RIS cannot optimally operate passive beamforming. To overcome the aforementioned problems, in this paper we aim to leverage the Fresnel zone characteristic and the position information of vehicles to design a position-aided on/off states judgment (POSJ) scheme for practical 1-blt RIS. The proposed scheme does not require instantaneous channel state information at the transmitter (CSIT) and is based on the superposition of the reflected signals from different Fresnel zone boundaries. Simulation results indicate that our developed scheme performs well with the increase in positioning accuracy, thus enabling the practical application of RIS.

As a visible light communications (VLC) modulation method, the layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) is promising. The layers in LACO-OFDM provide a high flexibility for power allocation and modulation selection. In this paper, we analyze the impacts of LACO-OFDM layer selection, layer power allocation and modulation selection. Firstly, we compare the BER performance of different power allocation methods and investigate the impact of layers on BER and optical power. On this basis, we propose a joint layer selection and power allocation algorithm. Simulation results show that the proposed power allocation scheme has a better bit error ratio (BER) performance than the common schemes. Finally, we verify the layer modulation selection and propose a search method with low time complexity.

As the transmitter in visible light communications (VLC) system, when the electrical current signal exceeds the input range of light emitting diode (LED), it will cause a nonlinear distortion. Since the electrical current is proportional to the optical power, the nonlinear distortion of electrical current signal is equivalent to the nonlinear distortion of optical power signal. This paper studies the impacts of optical power signal nonlinear distortion on signal distribution and capacity. Firstly, we derive the maximum entropy distribution of the optical power signal and obtain the corresponding capacity lower bound using the entropy power inequality (EPI). Then the nonlinear distortion is modelled as the signal clipping noise, and we analyze the impacts of upper-clipping and lower-clipping on system performance. Numerical results show that a certain degree of signal clipping is acceptable at low optical signal-to-noise ratio (OSNR). However, the clipping should be minimum at high OSNR.

In visible light communications, adding direct current (DC) bias is the most common solution to the bipolar problem of signal, so that the precoding and optical orthogonal frequency division multiplexing (O-OFDM) can be performed. Moreover, it will be easier to achieve dimming control. In this paper, we analyze the impact of DC bias on optical power signal in the view of distribution, capacity and nonlinear distortion. Firstly, we classify the impacts of DC bias into two types depends on the total power, and derive their maximum entropy distributions. Then we investigate the capacity of two different channels with DC bias. Finally, we study the nonlinear distortion of power signal. Derivation and simulation results show that the capacity would decrease as the DC bias increases in most cases. The conclusions will help the consideration of DC bias in future system design.

As one of the potential candidate technologies for sixth generation (6G), visible light communication (VLC) can provide indoor communication services with high data rate when combined with multiple-input multiple-output (MIMO). In this paper, we first study the impact of precoding on zonotopes model of channel and then derive the capacity of VLC-MIMO with precoding when the number of transmitters is greater than receivers. Based on the zonotopes graph, two design principles for VLC-MIMO optimal precoding are obtained. Simulation results show that the commonly used radio frequency (RF)-MIMO precodings can hardly catch the capacity lower bound. The optimal precoding in RF may not be optimal in VLC.

In this article, an effective energy-efficiency optimization problem is investigated in the cognitive unmanned aerial vehicle (UAV) communication system, where the moving following UAV (FUAV) transmits collected data to the leading UAV (LUAV) by reusing the spectrum of the ground primary user. For this scenario, a novel joint UAV trajectory and resource allocation optimization algorithm is proposed. We aim to maximize the energy efficiency of the cognitive UAV communication systems under interference, shortest step size, collision prevention, and speed constraints. However, this optimization problem is difficult to be solved, as it is nonconvex and involves strong relations among many variables. To address this issue, we first decompose the original optimization problem into four subproblems: 1) spectrum sensing duration subproblem; 2) spectrum sensing threshold subproblem; 3) transmitted power subproblem; and 4) FUAV trajectory optimization subproblem. For spectrum sensing duration subproblem and spectrum sensing threshold subproblem, their optimal solutions can be efficiently solved via a golden section search method. For the transmitted power subproblem, the closed-form expression of the optimal transmitted power is deduced. For the nonconvex FUAV trajectory optimization subproblem, we consider the shortest step size design under known starting and destination positions and then obtain an approximate solution of FUAV trajectory via the successive convex optimization (SCO) technique. Finally, an alternate iterative framework with fast convergence is designed to solve the original optimization problem. The simulation results show that the proposed algorithm has 18 times improvement in energy efficiency compared with the straight flight scheme with lower energy consumption.

In this article, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and a RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time varying and the system is high dimensional, this nonconvex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of the neural network can be accelerated with appropriate neural network parameters.

Platooning strategy is an important part of autonomous driving technology. Due to the limited resource of autonomous vehicles in platoons, mobile-edge computing (MEC) is usually used to assist vehicles in platoons to obtain useful information, increasing its safety. Specifically, vehicles usually adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit large amount of data to the base station (BS) through vehicle-to-infrastructure (V2I) communications, where the useful information can be extracted by the edge server connected to the BS and then sent back to the vehicles to make correct decisions in time. However, vehicles may be moving on different lanes with different velocities, which incurs the unfair access due to the characteristics of platoons, i.e., vehicles on different lanes transmit different amount of data to the BS when they pass through the coverage of the BS, which also results in the different amount of useful information received by various vehicles. Moreover, age of information (AoI) is an important performance metric to measure the freshness of the data. Large average age of data implies not receiving the useful information in time. It is necessary to design an access scheme to jointly optimize the fairness and data freshness. In this article, we formulate a joint optimization problem in the MEC-assisted V2I networks and present a multiobjective optimization scheme to solve the problem through adjusting the minimum contention window under the IEEE 802.11 DCF mode according to the velocities of vehicles. The effectiveness of the scheme has been demonstrated by simulation.

Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power to process partial tasks through offloading and the remaining tasks through local execution. During the offloading, each VU adopts the multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) channel to improve the channel spectrum efficiency and capacity. However, the channel condition is uncertain due to the channel interference among VUs caused by the MIMO-NOMA channel and the time-varying path loss caused by the mobility of each VU. In addition, the task arrival of each VU is stochastic in the real world. The stochastic task arrival and uncertain channel condition affect greatly on the power consumption and latency of tasks for each VU. It is critical to design an optimal power allocation scheme considering the stochastic task arrival and channel variation to optimize the long-term reward, including the power consumption and latency in the MIMO-NOMA VEC. Different from the traditional centralized deep reinforcement learning (DRL)-based scheme, this article constructs a decentralized DRL framework to formulate the power allocation optimization problem, where the local observations are selected as the state. The deep deterministic policy gradient (DDPG) algorithm is adopted to learn the optimal power allocation scheme based on the decentralized DRL framework. Simulation results demonstrate that our proposed power allocation scheme outperforms the existing schemes.

Traditional Canny edge detection algorithm has been widely used for the extraction of hyperbolic edges in ground penetrating radar (GPR). However, the Canny edge detection algorithm cannot adaptively determine the segmentation thresholds. Moreover, multiple hyperbolic edges are usually detected for each target, which increases the complexity of subsequent processing. In this letter, we propose a novel method that obtains only one hyperbolic edge for each target and automatically determines the segmentation threshold. Both simulation and real data results show that the proposed method is able to extract the target hyperbolic edges precisely and significantly improves the efficiency of target detection combined with the Hough transform in impulse GPR systems.

This letter proposes a nonparametric detector, termed as Gini Correlation (GC), to solve the classical problem of detecting deterministic signals buried in impulsive noise. With the help of the popular Middleton’s Class-A impulsive noise (MCAN) model, we derive the expectation and variance of GC under alternative hypothesis and null hypothesis, which, along with the central limit theorem, are further employed for determining the detection probability and the detection threshold. The results show that the proposed detector possesses a constant false alarm rate property. Monte Carlo simulations verify not only the correctness of our theoretical findings but also the superiority of GC to other state-of-the-art methods in terms of receiver operating characteristic (ROC) curves and asymptotic relative efficiency (ARE) curves.

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

Reconfigurable intelligent surface (RIS) is a revolutionary technology and can be used to assist communication systems by adaptively manipulating the wireless environment. In this paper, we propose a novel cascaded channel estimation algorithm for the RIS-assisted multiple-input multiple-output orthogonal frequency division multiplexing systems. Inspired by the channel compression idea, we can obtain a sub-sampled channel by turning off a fraction of RIS elements and then extrapolate it to the complete one, by which the pilot overhead is greatly reduced. The problem of channel extrapolation is transformed into recovering the physical parameters of the cascaded channel from the partial channel observations and is then formulated by the sparse Bayesian learning (SBL) framework. In order to circumvent the curse of high dimensional matrices inversion in the vector-matrix system, we further introduce the tensor structure into the SBL framework. Specially, by leveraging of the channel sparsity over the angle domain and delay domain, we derive the virtual channel expression in tensor form and model a Kronecker-structured prior distribution for the virtual channels. The multi-domain sparse properties of the virtual channel tensor can be effectively captured by a group of low-dimensional hyper-parameters, and thus reduce the computational complexity. In addition, the Cramer-Rao lower bound is derived for the proposed channel extrapolation. Simulation results show the superior performance of the proposed scheme.

This paper studies a convincingly secure transmission framework under an allowable outage probability for practical finite-alphabet inputs, where a full-duplex receiver (Bob) is taken into account to emit the artificial noise for deteriorating the eavesdropper’s decoding performance. We develop a secure-reliable mechanism to take the place of prior secrecy rate (SR) maximization strategy, where a closed form expression is invoked for substituting the non-closed SR expression upon exploiting the multi-exponential decay fitting approach. Hence, the intractable expectation operation over a large number of noise samples is circumvented. Moreover, a pair of critical probabilities of the reliable transmission and secure outage are first analyzed, and then a low-complexity optimization scheme is formulated. Apart from designing the transmission scheme for classically secure networks consisting of a transmitter, Bob and an eavesdropper, we further carry out an investigation on conceiving a secure-reliable strategy against multiple Eves for improving the system’s extensibility. To this end, analytical expressions of both the reliability outage and secrecy outage probabilities for multiple-Eve scenarios are also derived. Furthermore, a pragmatic iterative solution is conceived for addressing the corresponding max-min optimization problem. Finally, the simulation results validate the significance of our considered secure-reliable transmission in terms of the average SR performance attained.

The application of non-orthogonal multiple access (NOMA) to multicarrier systems can improve the spectrum efficiency and enable massive connectivity in future mobile systems. Resource allocation in multicarrier NOMA systems is a non-deterministic polynomial time-hard problem requiring exhaustive search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed. In this paper, exact values of the optimal channel gain ratios between a pair of NOMA users are presented for the first time for quadrature amplitude modulation (QAM) schemes. Further, numerical limits are derived for the values of channel gain ratios that fulfill the system constraints. These findings are used to propose a user pairing algorithm with quasi-linear complexity. Further, a novel scheme for data rate and continuous power allocation is proposed. Through numerical simulations, it is proved that the proposed scheme yields an achievable sum-rate close to the performance of exhaustive search, and it outperforms other suboptimal resource allocation schemes.

With the very stringent demand for real-time transmission of wireless communication services, the requirement of sub-millisecond ultra-low end-to-end delay has been initially proposed in the sixth generation (6G) communication networks. Finite blocklength transmission is one of the potential technologies to meet such a low end-to-end delay demand for the next generation networks. However, as the finite blocklength decreases, the transmission delay decreases while the queuing delay increases, which results in the tradeoff between the transmission delay and the queuing delay. To achieve the optimal balance, in this paper we propose an adaptive blocklength transmission framework to minimize the important part of the end-to-end delay of wireless networks, where we focus on the transmission delay and queuing delay. A dynamic buffering model for variable transmission time interval (V-TTI) is introduced for the time-varying arrival of packets adaptation. Then, we propose the Flexible proximal Alternating direction method of multipliers based Blocklength Optimization (FaBo) scheme to minimize the important part of the end-to-end delay for the single user case. We also propose the Multiple deep Q-learning network based Resource Allocation (MuRa) scheme, which can efficiently balance the transmission delay and queuing delay, to minimize the important part of the end-to-end delay for the multi-user case. Numerical results show that the proposed adaptive blocklength framework can reduce the important part of the end-to-end delay compared with that of long-term evolution and the fifth generation (5G) new radio. We also show that our proposed schemes can quickly converge to the minimum end-to-end delay.

This paper proposes a new simultaneous terahertz (THz) information and power transfer (STIPT) system, which utilizes a reconfigurable intelligent surface (RIS) for both the data and power transmission. We aim to maximize the information users’ (IUs’) sum data rate while guaranteeing the power harvesting requirements of energy users (EUs) and RIS. To solve the formulated non-convex problem, the block coordinate descent (BCD) based algorithm is adopted to alternately optimize the transmit precoding of IUs, RIS’s reflecting coefficients, and the position of RIS. Additionally, the penalty constrained convex approximation (PCCA) algorithm is proposed to optimize the deployment of the RIS, where the introduced penalties ensure that the solution is always feasible. The simulation results show that the proposed solution outperforms the benchmark schemes, and the proposed BCD algorithm can greatly improve the performance of the STIPT system.

In this paper, the performance analysis of non-orthogonal multiple access (NOMA) in a cloud radio access networks (C-RAN) is carried out. The problem of jointly optimizing user association, muting and power-bandwidth allocation is formulated for NOMA-enabled C-RANs. To solve the mixed integer programming problem, the joint problem is decomposed into two subproblems as 1) user association and muting 2) power-bandwidth allocation optimization. To deal with the first subproblem, we propose a centralized and heuristic algorithm to obtain a feasible solution to the remote radio head (RRH) muting problem for given bandwidth and transmit power. The second subproblem is then reformulated for tractibility purpose and a low-complexity algorithm is proposed to bandwidth and power allocation subject to users data rate constraints. Moreover, for given user association and muting states, the near optimal power allocation is derived in a closed-form expression. Simulation results show that the proposed NOMA-enabled C-RAN outperforms orthogonal multiple access (OMA)-based C-RANs in terms of total achievable rate, interference mitigation and can achieve significant fairness improvement.

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

The application of non-orthogonal multiple access (NOMA) to multicarrier (MC) systems can improve the spectrum efficiency and enable massive connectivity in future mobile communication systems. Resource allocation in MC NOMA is a non-deterministic polynomial time (NP)-hard problem with prohibitive computational complexity. Thus, efficient algorithms that provide a good trade-off between performance and complexity are needed. In this paper, exact values of the optimal channel gain ratios between a pair of NOMA users are presented for quadrature amplitude modulation (QAM). Further, numerical limits are derived for the values of channel gain ratios that fulfill the system constraints. Unlike previous works, it is demonstrated that the benefit of pairing users with very distinct channel gains is lost under practical QAM schemes, due to the inability of users with poor channel conditions to fulfill bit error rate (BER) constraints. These findings are used to propose a user pairing algorithm with quasi-linear complexity. Further, a novel scheme for data rate and continuous power allocation is proposed. Through numerical simulations, it is proved that the proposed scheme yields an achievable sum-rate close to the performance of exhaustive search, and it outperforms other suboptimal resource allocation schemes.

Recently, deep learning-based algorithms have brought impressive results in deblurring tasks. However, as an image prior proved important in image restoration tasks, self-similarity was not exploited in motion deblurring. To tackle this problem, we propose an Attention Self-Similarity Capture (ASSC) module, which takes full advantage of self-similarity by capturing long-range feature dependencies. Besides, to achieve a trade-off between performance and efficiency, we design an Enhanced Spatial Attention (ESA) module, which can dynamically adapt to the spatially-varying motion blur. We employ patch-hierarchical architecture composed of the two modules mentioned above with parameter-free feature flow between different levels. Moreover, we build two large-scale datasets, GOPRO-Supplement and SONY-Extension, to expand the public GOPRO dataset’s scene and resolution. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on both the public GOPRO dataset and our datasets.

Reconfigurable intelligent surface (RIS) is an emerging technique employing metasurface to reflect the signal from the source node to the destination node. Essentially, RIS can be considered as a passive relay between the source and destination node. By contrast, relay node in a traditional network has to be active, consuming energy when it is relaying the signal or information. It is worth investigating the differences between RIS and relay. In this paper, we compare the performances between RIS and active relay for a general multiple-input multiple-output (MIMO) system. To make the comparison fair and comprehensive, both the performances of RIS and active relay are optimized with best-effort, by formulating a non-convex optimization problem respectively. In case of RIS, transmit beamforming and reflecting coefficients are jointly optimized. In case of active relay including half-duplex relay and full-duplex relay, transmit beamforming for both source and relay are optimized. The original nonconvex optimization problems are transformed into to a weighted mean-square error (MSE) minimization problem and solved by the proposed alternating optimization algorithm, respectively. Numerical results are presented to demonstrate the validity of the proposed algorithm.

In this paper, we propose the robust transmission design for an intelligent reflecting surface (IRS) aided distributed multi-antenna system under imperfect cascaded channel state information at transmitter (CSIT). The active transmit beamforming at access points and reflection coefficients at IRS are jointly optimized to maximize the worst-case sum rate subject to the transmit power constraint and unit-modulus reflection coefficients for all possible channel realizations in the uncertainty region. We propose the conservative approximation (CA) based robust optimization. Under the framework of alternative optimization algorithm, the transmit beamforming is first solved via weighted minimum mean square error method with the fixed reflection coefficients, and then the reflection coefficients are optimized via nearest point projection method. Since the CA method can introduce the rate saturation for non-scaling CSIT as signal-to-noise ratio scales, we investigate another robust optimization algorithm, cutting-set (CS) method, which solves the problem by alternating between an optimization step for a finite subset of the uncertainty region, and a pessimization step for updating the subset. The numerical simulation results illustrate that the CS method can reap better sum-rate performance than CA, and the deleterious effect of imperfect cascaded CSIT on the performance becomes more significant with the increase of the reflecting elements.

This letter proposes to apply two rank correlations, namely, Kendalls tau (KT) and Spearmans rho (SR), to the fundamental problem of detecting known signals in impulsive noise. Under the popular Middletons Class-A impulsive noise model, we derived the expectations and variances of KT and SR, which, along with the central limit theorem, are further employed to determining the detection threshold and the detection probability. Monte Carlo simulations not only verified the correctness of our theoretical findings but also demonstrated the superiority of KT and SR to the other five state-of-the-art methods in terms of detection probability.

In the fifth generation (5G) era, mobile networks will provide diversified services such as enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communication (URLLC). Furthermore, the 5G architecture will be fog-like, enabling a functional split of network functionalities between cloud and edge nodes. In this article, we propose a non-orthogonal multiple access (NOMA)-enabled fog-cloud structure in a novel density-aware fog-based radio access networks (F-RAN) to tackle different aspects of high and low-density regions, in order to meet the heterogeneous requirements of eMBB and URLLC traffic. A framework of two different problem formulations is considered to cater the high throughput and low-latency requirements in a high and low-density mode respectively. In the first problem, we study the joint caching placement and association strategy aiming at minimizing the average delay. To deal with the first problem, we apply McCormick envelopes and Lagrange partial relaxation method to transform it into three convex sub-problems, which is then solved by proposing a distributed algorithm. The second problem is to jointly optimize transmission mode selection, subchannel assignment and power allocation to maximize the sum data rate of all fog user equipments (F-UEs) while satisfying fronthaul capacity and fog-computing access point (F-AP) power constraints. Moreover, for given transmission mode selection and subchannel assignment, the optimal power allocation is derived in a closed-form expression. Simulation results are provided to validate the effectiveness of the proposed NOMA-enabled F-RAN framework and reveal that the ultra-low latency and high throughput can be achieved by properly utilizing the available resources.

Intelligent reflecting surface (IRS) is of low-cost and energy-efficiency and will be a promising technology for the future wireless communications like sixth generation. To address the problem of conventional directional modulation (DM) that Alice only transmits single confidential bit stream (CBS) to Bob with multiple antennas in a line-of-sight channel, IRS is proposed to create friendly multipaths for DM such that two CBSs can be transmitted from Alice to Bob. This will significantly enhance the secrecy rate (SR) of DM. To maximize the SR (Max-SR), a general non-convex optimization problem is formulated with the unit-modulus constraint of IRS phase-shift matrix (PSM), and the general alternating iterative (GAI) algorithm is proposed to jointly obtain the transmit beamforming vectors (TBVs) and PSM by alternately optimizing one and fixing another. To reduce its high complexity, a low-complexity iterative algorithm for Max-SR is proposed by placing the constraint of null-space (NS) on the TBVs, called NS projection (NSP). Here, each CBS is transmitted separately in the NSs of other CBS and AN channels. Simulation results show that the SRs of the proposed GAI and NSP can approximately double that of IRS-based DM with single CBS for massive IRS in the high signal-to-noise ratio region.

Spatial domain modulation employs spatial degree of freedom to convey information bits. Traditional spatial domain modulation schemes include spatial modulation, spatial shift keying (SSK), and variations of these two schemes. To fully make use of the spatial degree of freedom and improve the achievable data rate of traditional spatial domain modulation schemes, we propose a successive-coded SSK (SC-SSK) modulation scheme. The core of the proposed SC-SSK scheme is a multiple step coded modulation strategy, in which different information bits are mapped to the designed pattern in each step. The constellation of the transmit signals is designed. A QR decomposition based successive detection algorithm is proposed for this particular coded modulation strategy. The achievable rate, the error rate performance, and the computational complexity of the proposed SC-SSK scheme are analyzed in detail. Simulations are carried out to compare the proposed the SC-SSK scheme with the reference spatial domain modulation schemes, and reveal the advantages of the proposed SC-SSK scheme.

Technically, the security performance of generalized spatial modulation (GenSM) networks can be enhanced by dynamically adjusting the precoder allocated to the legitimate signal as communication channel varies. For this purpose, our paper proposes a secure transmission strategy upon designing both digital and analog precoders for hybrid GenSM systems, where an eavesdropper is taken into account. The concept of the hybrid GenSM system has arose to improve the spatial multiplexing (SMX) gain for remedying the shortcoming of the limited number of radio frequency chains in traditional GenSM systems. However, this may lead to a great deal of security degradation since the SMX gain of the unintended receiver will be also improved. To this end, we develop a secrecy enhancement scheme by devising both analog and digital precoders for hybrid GenSM networks. Specifically, we derive an efficiently closed-form alternative to the original secrecy rate (SR) expression for reducing the excessive computational complexity of the joint optimization problem over the hybrid precoder. Then, by using this alternative as our cost function, an iterative algorithm is proposed. In particular, we elaborately conceive a pair of concave maximization problems in order to optimize the digital and analog precoders, respectively. Our proposed strategy not only utilizes semi-positive definite relaxing technique over the analog precoder but also invokes a lower bound of the alternative to further simplify the optimization over the vectored digital precoder. Subsequently, both the convergence and computational complexity of the proposed alternating iteration algorithm are analyzed. Compared to existing designs, our proposed strategy strikes a compelling role in balancing the SR performance and complexity. Finally, our simulation results confirm the efficiency of the proposed algorithm in terms of the SR performance achieved.

Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.

Network-assisted full-duplex (NAFD) distributed massive multiple-input multiple-output (MIMO) systems enable simultaneous uplink and downlink communications by dynamically allocating the numbers of uplink and downlink remote antenna units (RAUs), which potentially improve the spectral efficiency in wireless communications. In such systems, channel state information (CSI) plays a critical role in uplink reception and downlink transmission, as well as the cross link interference cancelation caused by downlink RAUs to uplink RAUs. Moreover, downlink terminals need to estimate CSI to reliably decode the received signals due to the reduced channel hardening effect. However, high training overhead makes it generally impossible to directly estimate CSI. This paper proposes to estimate effective CSI (inner products of beamforming and channel vectors) instead based on beamforming training scheme. Under this scheme, we derive closed-form expressions for uplink and downlink achievable rates with different receivers and beamforming. Given these expressions, we propose an efficient power allocation scheme which is only dependent on slowly varying large-scale fading from the perspective of multi-objective optimization. Numerical results verify the accuracy of the derived closed-form expressions and effectiveness of beamforming training based CSI estimation. Moreover, trade-off regions between the considered optimization objectives under various system parameters offer numerous flexibilities for system optimization.

In this paper, a cooperative task computation framework exploits the computation resource in user equipments (UEs) to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs' power consumption and the penalty of unaccomplished tasks, and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework.

Spatial modulation (SM) is a particularly important form of multiple-input multiple-output (MIMO), which uses both modulation symbols and antenna indices to carry information. In this paper, to avoid the high cost and circuit complexity of SM, we consider the hybrid SM system with a hybrid precoding transmitter architecture, combining a digital precoder and an analog precoder. In such a system, we carried out secure hybrid precoding and transmit antenna subarray selection (TASS) methods. Two hybrid precoding methods, called maximizing approximate secrecy rate (SR) via gradient ascent (Max-ASR-GA) and maximizing approximate SR via alternating direction method of multipliers (Max-ASR-ADMM), are proposed to improve the SR performance. As for TASS, a high-performance method of maximizing the approximate SR (Max-ASR) is first presented. To reduce its high complexity, two low-complexity TASS methods, namely maximizing eigenvalue (Max-EV) and maximizing product of signal-to-interference-plus-noise ratio and artificial noise-to-signal-plus-noise ratio (Max-P-SINR-ANSNR), are proposed. Simulation results demonstrate that the proposed Max-ASR-GA and Max-ASR-ADMM hybrid precoders harvest substantial SR performance gains over existing method. For TASS, the proposed three methods Max-ASR, Max-EV, and Max-P-SINR-ANSNR perform better than existing leakage method. Particularly, the proposed Max-EV and Max-P-SINR-ANSNR are of low-complexity at the expense of a little performance loss compared with Max-ASR.

Fog-computing network combines the cloud computing and fog access points (FAPs) equipped with mobile edge computing (MEC) servers together to support computation-intensive tasks for mobile users. In this paper, we investigate the delay minimization problem for task offloading in a hierarchical fog-computing C-RAN network, which consists of three tiers of computational services: MEC server in radio units, MEC server in distributed units, and the cloud computing in central units. The receive beamforming vectors, task allocation, computing speed for offloaded tasks in each server and the transmission bandwidth split of fronthaul links are optimized by solving the formulated mixed integer programming problem. The simulation results validate the superiority of the proposed hierarchical fog-computing C-RAN network in terms of the delay performance.

In vehicular communication systems, maximizing the number of served vehicles while simultaneously guaranteeing reliable coverage at all the vehicles can be a challenging proposition. A switched-beam based infrastructure can provide better reliability as the signal-to-interference-plus-noise ratio (SINR) can be improved. However, a simple switched-beam based vehicle-to-infrastructure (V2I) system alone may not suffice for serving all the vehicles because (i) the number of vehicle is more than the number of beam, and (ii) a vehicle may be out of the coverage region of a beam. Therefore, introducing vehicle-to-vehicle (V2V) communication becomes crucial in extending the number of served vehicles. In this paper, ajoint vehicle-beam allocation (VBA) and vehicular proximity (VP) algorithms for V2I and V2V, respectively are proposed to guarantee reliable coverage for vehicles. VBA is an SINR optimization algorithm, and VP is based on LTE Mode 4, a proximity based service for V2V communications. It is shown that setting a flexible SINR threshold helps in attaining a reliable beam coverage region in switched-beam based V2I communication. It is proven that the outage probability are also directly dependent on SINR thresholds. Lastly, the concept of utility ratio is also introduced as a metric for reliability. Simulation results show that joint V2I and V2V communication significantly improves the utility ratio.

Autonomous driving in future vehicle system has imposed a high demand for reliable and power efficient communication to provide safe-driving to vehicular users. In order to achieve reliability and power efficiency, in this paper, a vehicular trellis architecture (VTA) is proposed. VTA allows traffic information to be transmitted either via vehicle-to-infrastructure (V2I), through the dedicated remote radio head (RRH) and its dynamically select vehicle(s) or, direct vehicle-to-vehicle (V2V) communication. We investigated the adaptive switching over V2I/V2V, to enhance efficient system reliability while optimizing the average power consumption of VTA. This paper presents a vehicular trellis algorithm (VTRA), which approximate solutions to the optimization problem. Simulation results demonstrate that the selection of joint V2I/V2V communication reduces the total power consumption of the system for a varying number of threshold distance and vehicles.

The problem of resource allocation in multicarrier non-orthogonal multiple access (NOMA) systems is non-deterministic polynomial time (NP)-hard and requires exhaustive search, which has prohibitive computational complexity. Computationally-efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into two sub-problems, namely subcarrier assignment and power allocation. The optimal users' channel condition relationship is derived in terms of effective system sum-rate, and a computationally-efficient ideal partner search algorithm (IPSA) is proposed. Further, optimal power allocation (OPA) is studied in terms of fairness, and an OPA coefficient is theoretically derived based on the channel condition ratio of two NOMA users. Numerical results show that IPSA with OPA outperforms IPSA with fractional transmit power allocation (FTPA), the strongest user-weakest user (SUWU) scheme and user grouping (UG) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER. Further, the computational complexity of IPSA with OPA is much lower than that of exhaustive search.

Rapid deployment is crucial for building up drone aided emergency communications to ensure the coverage and service support after the disaster. The chaos in the post-disaster area, such as the number of ground users and scattered locations, makes difficult on the decision of drone deployment. In this paper, an unsupervised machine learning method is conducted for drone deployment in drone aided emergency communications. Considering the importance of sustainable services for drones, the drone deployment problem is formulated with the aim of minimizing the total power of drones with all users' coverage while maintaining their rate requirements, with constraints on drones' coverage area, capacity and limited power. The problem is solved by two steps. A modified k-means clustering algorithm is proposed to obtain the number of drones and an optimal altitude and minimum transmit power algorithm is then derived. Simulation results show that although the number of drones obtained by the modified algorithm is more than that of the original k-means algorithm, all users are served and the minimum power of drones is guaranteed by proposed algorithms.

In this paper, the performance analysis of non-orthogonal multiple access (NOMA) in heterogeneous cloud radio access networks (H-CRAN) is carried out, where coordination of macro base station (MBS) and remote radio heads (RRHs) for H-CRAN with NOMA is introduced to improve network performance. We formulate the problem of jointly optimizing user association, coordinated scheduling and power allocation for NOMA-enabled H-CRANs. To efficiently solve this problem, we decompose the joint optimization problem into two subproblems as 1) coordinated scheduling 2) power allocation optimization. Firstly the users are divided based on interferences they suffer from. This interference-aware NOMA approach account for the inter-tier interference. Based on the user scheduling scheme, optimal power allocation optimization is performed by the hierarchical decomposition approach. Simulation results show that the proposed NOMA-enabled H-CRAN outperforms orthogonal multiple access (OMA)-based H-CRANs in terms of total achievable rate, interference mitigation and can achieve significant fairness improvement.

Since the analysis of cell coverage faces complex environments in unmanned aerial vehicle base station (UAV‐BS) systems, general coverage probability in a typical cell is derived to analyse a UAV‐BS multi‐tier network, and this coverage probability is closely related with power difference among UAV‐BSs, two‐dimension (2D) and three‐dimension (3D) UAV‐BS deployment, and general interference fading models. Especially, closed‐form coverage probability expressions are also derived in the form of 2D scenario and 3D scenario, when the interference fading coefficient is assumed to follow a Gamma distribution and an exponential distribution, respectively. It shows that there exists maximum spectral efficiency density (SED) as the BS density increases, and such a BS density achieving the max‐SED is independent of noise power, but is affected by various propagation parameters.

Covert communications can hide the very existence of wireless transmissions and thus are able to address privacy issues in numerous applications of the emerging Internet of Things (IoT). In this article, we adopt channel inversion power control (CIPC) to achieve covert communications in Rayleigh fading wireless networks, where a transmitter can possibly hide itself from a warden while transmitting information to a receiver. The CIPC can guarantee a constant signal power at the receiver, which removes the requirement that the receiver has to know the channel state information in order to coherently decode the transmitter's signal. Specifically, we examine the performance of the achieved covert communications in terms of the effective covert rate (ECR), which quantifies the amount of information that the transmitter can reliably convey to the receiver subject to the warden's total error probability being no less than some specific value. The noise uncertainty at the warden serves as the enabler of covert communications. For fairness, we also consider noise uncertainty at the legitimate receiver. Our examination shows that increasing the noise uncertainty at the warden and the receiver simultaneously may not continuously improve the ECR achieved in the considered system model.

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

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.

In this paper, we investigate energy efficient transmission for a satellite-aerial-terrestrial network (SATN), where a multi-antenna unmanned aerial vehicle (UAV) is employed as a relay to assist the satellite signal delivery. By considering total power constraint (TPC) or per-antenna power constraint (PPC) at the UAV, we first formulate an optimization problem to maximize the energy efficiency of the SATN, which is defined as ratio of the ergodic capacity to the total power consumption for communication at UAV. Then, by jointly exploiting array signal processing with the Dinkelbach's method, two new beamforming (BF) schemes, namely, TPC-BF and PPC-BF are proposed to solve the non-convex energy efficiency maximization problem. The main advantage of our method is that only angular information-based channel state information is used to obtain BF weight vectors so that a low implementation complexity is achieved. Furthermore, by assuming that the satellite-UAV link undergoes correlated Shadowed-Rician fading while the UAV-terminal link experiences correlated Rician fading, closed-form expressions for the statistics of the equivalent output signal-to-noise ratio are derived and, thus energy efficiency for the considered SATN with BF schemes is analytically presented. Finally, simulation results corroborate the derived expressions and confirm the effectiveness of the proposed BF schemes.

In this paper, the spectral efficiency of network assisted full-duplex communications (NAFD) in cell-free (CF) massive multiple-input multiple-output (MIMO) network with imperfect channel state information is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error receiver as well as the downlink sum-rate with zero-forcing and regularized zero-forcing beamforming are presented. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex remote antenna units offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.

Data dissemination to multiple users within a predetermined deadline is a commonly required function in many emerging wireless scenarios. In this paper, we propose a data dissemination scheme enhanced by device-to-device (D2D) communications and random linear network coding (RLNC), where the broadcast and the D2D links are used simultaneously with on-the-fly RLNC to accelerate the dissemination. The probability distribution of the completion time of using either pure or systematic RLNC is analyzed assuming a finite field size, under a general model where the cooperation may occur probabilistically and multiple packets may be exchanged between the D2D users in each time slot. An optimization problem is formulated to design the cooperation parameters to minimize the expected energy consumption of the system. Numerical and simulation results show that the analysis is accurate, and that to minimize the energy consumption a tradeoff exists between allowing for longer broadcast and for more intensive cooperation. The cooperation parameters designed for multiple D2D pairs are shown to be effective for meeting the reliability requirements.

In intensity-modulation and direct-detection (IM/DD) multiple-input and multiple-output (MIMO) visible light communication (VLC) systems, spatial subchannels are usually correlated, and spatial modulation is a good choice to achieve the advantages of MIMO technology. Peak-to-average power ratio (PAPR) is a key issue in VLCs due to the limited linear dynamic range of light emitting diodes (LEDs). Single-carrier communication systems have a lower PAPR than orthogonal frequency division multiplexing (OFDM) communication systems. However, it is challenging to design a single-carrier spatial modulation for high-rate transmissions because of the time domain intersymbol interference. This paper develops an optical spatial modulation (OSM) scheme based on bipolar pulse amplitude modulation (PAM) and spatial elements for high-rate indoor VLC systems. Multiple data streams can be transmitted simultaneously in the proposed scheme. Based on the transmit strategy, we develop a low-complexity receiver algorithm that achieves better bit-error rate performance than reference schemes, and the proposed OSM scheme has a much lower PAPR than OFDM based OSM schemes. When the spatial subchannels are highly correlated, a spatial area division strategy is applied, and the receiver algorithm is investigated. The symbol-error rate expression of the proposed OSM scheme is derived, and the computational complexity is analyzed.

To support millimeter-wave (mmWave) communications successfully, a large number of antennas (in the order of hundreds or thousands) must be implemented to mitigate significant propagation and scattering losses. Designing phased arrays for carrier frequency is a great method for narrowband systems, but the performance degrades significantly with larger bandwidth. We show that as the system bandwidth increases, the beams steer away from the focus direction, which is an effect known as beam squint in wideband systems. In this paper, at first, analysis of capacity is performed for an increasing number of antennas, to show the significance of beam squint. Then, a solution in digital domain is proposed. Conventionally, a single beam would be allocated to a user. By exploiting beam squint effect, in this solution more than one beam can carry a single-user’s data, which improves the system’s performance significantly, especially when the number of antennas in an array is large and there are multiple users.

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the non-cooperative scheme.

With low cost and high mobility, drones are envisioned to be an important contributor to emergency communications. In this paper, we consider a unique drone aided emergency communication deployed in hills and mountains areas, where power-constrained drones serve as relays to improve the uplink sum rates via amplify-and-forward (AF) relaying. The channel models between drones and ground users in this communications and the ground users distributions are greatly different from that in conventional relay networks, while drones have their coverage areas and capacity constraint. By taking these specific characteristics into account, we formulate the joint power and subcarrier allocation problem to maximize the uplink throughput in the drone aided emergency communications under constrains to the transmit power budget for each drone and the number of accessed users on each subcarrier. Due to the intractability of the formulated problem, it is decomposed into two subproblems: power allocation optimization and subcarrier allocation optimization. Then a joint resource allocation algorithm is proposed. The simulation results show that the proposed algorithm outperforms the others.

In this paper, a large-scale distributed antenna system (DAS) with network-assisted full duplexing (NAFD) is investigated. Our model assumes that imperfect channel state information (CSI) is available and channels have independent spatial correlations. Based on large dimensional random matrix theory (RMT), the deterministic equivalent expressions for the ergodic uplink (UL) sum-rate with minimum-mean-square-error (MMSE) receiver as well as the ergodic downlink (DL) sum-rate with regularized zero-forcing (RZF) beamforming are derived. With the cancellation of downlink-to- uplink interference, the spectral efficiency of NAFD system is further improved. Numerical simulations indicate that under various environment settings, the deterministic equivalent results are accurate in both large-scale system and system with finite number of antennas.

The problem of resource allocation in non-orthogonal multiple access (NOMA) systems is nondeterministic polynomial time (NP)-hard and requires exhaustive search. Hence, fast and computationally efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into the sub-problems of subcarrier assignment and power allocation. The optimal users' channel condition relationship is derived in terms of the effective system sum-rate, and a fast ideal partner search algorithm (IPSA) is proposed in terms of that relationship. The performance of IPSA is verified under fractional transmit power allocation (FTPA). Simulation results show that our algorithm outperforms the strongest user-weakest user (SUWU) scheme, user grouping (UG) and random subcarrier and power allocation (RSPA) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER.

We consider an underlay wiretap cognitive two-way relay network (CTWRN), where two secondary sources exchange their messages via multiple secondary decode-and-forward digital network coding relays in the presence of an eavesdropper by using a three-phase time division broadcast protocol and sharing the licensed spectrum of primary users. To mitigate eavesdropping attacks, an artificial noise (AN)-aided opportunistic relay selection scheme, called generalized max-min (GMM) relay selection is proposed to enhance physical layer security for the wiretap CTWRNs. The performance of the GMM scheme is analyzed, and evaluated by the exact closed-form outage probability and intercept probability. Additionally, we also provide asymptotic approximations for the outage probability and intercept probability at high signal-to-noise ratio. For comparison, we analyze the performance of the conventional max-min (MM) relay selection scheme as well. It is shown that the GMM scheme outperforms the MM scheme in terms of the security-reliability tradeoff (SRT), where the security and reliability are quantified by the intercept probability and outage probability, respectively. Moreover, the SRTs of the MM and GMM schemes can be substantially improved by increasing the number of secondary relays, while the improvement of the GMM scheme is more evident than that of the MM scheme.

This paper examines the possibility, performance limits, and associated costs for a self-sustained relay to transmit its own covert information to a destination on top of forwarding the source's information. Since the source provides energy to the relay for forwarding its information, the source does not allow the relay's covert transmission and to detect it. Considering the time switching (TS) and power splitting (PS) schemes for energy harvesting, where all the harvested energy is used for transmission at the self-sustained relay, we derive the minimum detection error probability ξ* at the source based on which we determine the maximum effective covert rate ψ* subject to a given covertness constraint on ξ*. Our analysis shows that ξ* is the same for the TS and PS schemes, which leads to the fact that the cost of achieving ψ* in both the two schemes in terms of the required increase in the energy conversion efficiency at the relay is the same, although the values of ψ* in these two schemes can be different in specific scenarios. For example, the TS scheme outperforms the PS scheme in terms of achieving a higher ψ* when the transmit power at the source is relatively low. If the covertness constraint is tighter than a specific value, it is the covertness constraint that limits ψ*, and otherwise, it is upper bound on the energy conversion efficiency that limits ψ*.

Resource allocation of cellular-assisted device-to-device (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property of the D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and the D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among the sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity can be improved significantly by adopting the proposed resource allocation framework, especially in dense D2D deployed systems.

The mobility management will be more complex and will have a great impact on the quality of service in the future cellular networks, as these networks will have to handle a huge number of user equipments (UEs) and their frequent hand offs due to very dense short-footage small cells. This paper presents a framework to model and derive the coverage of small cells, the cell sojourn time, and the hand off rate in multi-tier small cell networks. The distribution of the small cells around a reference UE’s path is studied by taking into consideration the overlaps among the small cells. Two types of hand off rates are introduced to estimate the load managed by different cells, where inter-frequency hand off rate and intra-frequency hand off rate represent the fraction of hand offs managed by the first tier and the other tiers, respectively. Our analysis shows that ignoring the overlaps among the small cells affects the accuracy of the results significantly. The simulation results validate the accuracy of the analytical results and also show the impact of different parameters, such as the small cell density, the number of tiers and the size of the small cells on the small cell sojourn time, the macro cell sojourn time, and the hand off rate.

This paper deals with the challenging issue of the unaffordable channel training overhead in the dense cell-free massive multi-input multi-output (MIMO) system when a high number of users are being simultaneously served. By adopting the user-centric cluster method, a dynamic pilot reuse (DPR) scheme is proposed to allow a pair of users to share a single pilot sequence. Specifically, the proposed reuse scheme is achieved with the objective of maximizing the uplink achievable sum-rate subject to users' signal to interference plus noise ratio (SINR) requirements and pilot resources constraints. On this basis, the SINR expression is derived for any user sharing its pilot with another by utilizing both minimum mean squared error (MMSE) detection and channel estimation. A low complexity pilot reuse algorithm is then developed based on the separation distance between users. The iterative grid search (IGS) method is employed to find the threshold that can be utilized in the proposed algorithm to maximize the sum-rate. Finally, simulation results are presented to show the effectiveness of the DPR scheme with the optimized threshold in terms of the uplink achievable sum-rate.

Massive multiple-input-multiple-output (MIMO) is a promising technique to provide high-data-rate communication in fifth-generation (5G) mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed- beam based multiuser massive MIMO system where each user is served by a beam allocated to it. As the directions of fixed beams are predetermined and the users are randomly distributed, there could be some ``worst-case'' users, located at the edge of its serving beam, suffering from strong inter-beam interference and thus experiencing low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates without sacrificing the sum data rate.

One of the main targets of the fifth generation (5G) mobile communication systems and beyond is providing high data rate wireless transmissions and ubiquitous coverage to users. To support increasing subscriber populations in indoor environments, indoor wireless communication systems are required, but frequency spectrum allocations are limited and requires reuse of the limited spectrum. In order to design efficient and reliable indoor communication systems, a thorough understanding of the reuse distance is vital. This paper investigates a number of important issues associated with indoor radio propagation and frequency reuse planning in multi-storey buildings employing indoor distributed antenna system (DAS), where distributed remote antenna units (RAUs) supported by a central unit (CU) communicate with mobile equipment (MEs). In the DAS, co-channel interference caused by frequency reuse is a restraining factor as the frequency reuse distances are generally smaller. The effect of different reuse distances, penetration losses, pathloss exponents, and co-channel interference on spectral efficiency is analysed and performance evaluated over a wide range of potential ME locations.

In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques, including artificial noise (AN) projection, phase alignment/beamforming, and random subcarrier selection (RSCS) based on orthogonal frequency division multiplexing (OFDM), and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structure for the desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depends intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of the SINR and clarifying the wiretap region. Simulation and analysis show that the proposed scheme actually can achieve a secure and precise wireless transmission of confidential messages in line-of-propagation channel, and the derived theoretical formula of average secrecy rate is verified to coincide with the exact results well for medium and large scale transmit antenna array or in the low and medium SNR regions.

Next-generation network is considered as a device-to-device (D2D)-enabled system. The overlay in-band scheme can be used by the cellular user equipments and D2D user equipments (DUEs) to send data. The cellular and D2D links experience the in-band emission interference (IEI) from the DUEs that use the adjacent frequencies. This paper models the IEI impact by using stochastic geometry and analytically investigates this impact on cellular and D2D links. The IEI inter-cell and IEI intra-cell are separately assessed, and the expected D2D resource block (DRB) reuse factor is evaluated. Furthermore, the distance-density-based strategy is proposed to mitigate the IEI by controlling the number and location of served DUEs for each DRB. Also, the optimal power allocation algorithm is proposed by calculating the optimal DUEs transmission power profile that mitigates IEI and maximizes DUEs sum rate. The performance is improved significantly for the proposed methods. The application scenario is identified for each mitigation method.

Universal filtered multi-carrier (UFMC) is a potential waveform technology, which can efficiently combat different carrier frequency offsets (CFOs) of multiple users. First, adaptive modulation and power allocation are applied for each subcarrier to meet a preset bit error rate (BER) requirement by ignoring CFOs. Then, we prove that different CFOs will cause different interference variances to adjacent users, which results in performance degradation in UFMC systems. To reduce the interference caused by CFOs and improve achievable rate, a novel adaptive filter configuration algorithm is proposed to adaptively design the parameters of the finite impulse response filters. Specifically, the proposed algorithm is available for the UFMC systems, where the user equipments are allocated with different bandwidths. Finally, simulation results show that the proposed adaptive filter configuration algorithm can dramatically eliminate the interference caused by different CFOs, and achieve better BER performance and a higher achievable rate than the conventional scheme.

Covert wireless communication aims to hide the very existence of wireless transmissions in order to guarantee a strong security in wireless networks. In this paper, we examine the possibility and achievable performance of covert communication in amplify-and-forward one-way relay networks. Specifically, the relay is greedy and opportunistically transmits its own information to the destination covertly on top of forwarding the source's message, while the source tries to detect this covert transmission to discover the illegitimate usage of the resource (e.g., power and spectrum) allocated only for the purpose of forwarding the source's information. We propose two strategies for the relay to transmit its covert information, namely rate-control and power-control transmission schemes, for which the source's detection limits are analyzed in terms of detection error probability and the achievable effective covert rates from the relay to destination are derived. Our examination determines the conditions under which the rate-control transmission scheme outperforms the power-control transmission scheme, and vice versa, which enables the relay to achieve the maximum effective covert rate. Our analysis indicates that the relay has to forward the source's message to shield its covert transmission and the effective covert rate increases with its forwarding ability (e.g., its maximum transmits power).

Massive multiple-input-multiple-output (MIMO) has become a promising technique to provide high-data-rate communication in fifth-generation mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed-beam based multiuser massive MIMO system, where each user is served by a beam allocated to it. To maximize the sum data rate, a greedy beam allocation algorithm is proposed under the practical condition that the number of radio frequency chains is smaller than the number of users. Simulation results show that our proposed greedy algorithm achieves nearly optimal sum data rate. As only the sum data rate is optimized, there are some “worst-case” users, who could suffer from strong inter-beam interference and thus experience low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates and the max-min fairness among served users without sacrificing the sum data rate.

The overlay in-band device to device (D2D) scheme can be used by cellular user equipments (CUEs) and D2D user equipments (DUEs) to transmit the uplink and D2D data. The CUEs experience in-band emission interference (IEI) from the DUEs that transmit D2D signals in the adjacent channels. This paper evaluates the IEI impact in D2D-enabled cellular networks in terms of typical CUE outage probability for different DUE densities and DUE transmission powers. Further, the IEI intra-cell and IEI inter-cell are examined separately to determine the dominating part of IEI in the system. The results show the IEI impact has a harmful impact and causes the outage in the system with high probability when the DUE density is high. Also, a remarkable result finds that the IEI intra-cell and IEI inter-cell dominate the typical CUE outage probability similarly when the DUE density is low, whilst at high DUE density the IEI intra-cell does.

High-speed train (HST) has drawn considerable attention and become one of the most preferable conveyance mechanisms. Every year the manufacture corporations achieve a higher speed record and expect to attain 1000 km/h by 2021 using hyperloop one technology. Moving at such a high speed results in a high handover rate which makes it challenging for high speed railway (HSR) mobile wireless communication to preserve steady link performance. Employing distributed antenna systems (DASs) along with the two-hop architecture, this paper proposes a fast predictive handover algorithm. In this strategy, the serving cell starts the handover preparation phase in advance by inferring the train's current location. Issuing the handover preparation phase in advance reduces the handover latency and handover command failure probability as well. Lower handover command failure probability means lower handover failure probability which could greatly improve the end-users' quality of services (QoS). The analytical results show that the proposed scheme outperforms the conventional handover scheme.

Covert communication aims to shield the very existence of wireless transmissions in order to guarantee a strong security in wireless networks. In this work, for the first time we examine the possibility and achievable performance of covert communication in one- way relay networks. Specifically, the relay opportunistically transmits its own information to the destination covertly on top of forwarding the source's message, while the source tries to detect this covert transmission to discover the illegitimate usage of the recourse (e.g., power, spectrum) allocated only for the purpose of forwarding source's information. The necessary condition that the relay can transmit covertly without being detected is identified and the source's detection limit is derived in terms of the false alarm and miss detection rates. Our analysis indicates that boosting the forwarding ability of the relay (e.g., increasing its maximum transmit power) also increases its capacity to perform the covert communication in terms of achieving a higher effective covert rate subject to some specific requirement on the source's detection performance.

Previously, a scheme in [1] was proposed for the outdated channel state information (CSI) problem, for data transmission in time division duplex (TDD) systems. In user movement environment, the actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation. In this paper, the effect of different user mobility on TDD downlink multiuser distributed antenna system is investigated. An efficient autocorrelation based feedback interval technique is proposed and updates CSI at less the cost of the downlink time slots. In the proposed technique, the frequency of CSI feedback for different users is proportional to their speed. Cooperative clusters are formed to maximize sum rate where channel gain based antenna selection and user clustering based on signal-to- interference plus noise ratio (SINR) threshold is applied to reduce computational complexity. Numerical results show that sum rate superiority of the proposed scheme over the user mobility.

The cell sojourn time and the handoff rate have been considered as the main parameters in the mobility management of the cellular systems. In this paper, we address the mobility management in a small cell network and propose a framework to study the impact of different system parameters on the handoff rate and the small cell sojourn time. In the proposed framework, the overlapping coverage among the small cells and the number of overlaps on the path of a reference user equipment (UE) are derived to obtain the actual time that the reference UE spends in each small cell during its movement from the starting point to the destination point. The results validate the accuracy of the analysis in this paper in comparison to the analysis when ignoring the impact of the overlaps. The results also confirm the importance of considering the overlaps among the small cells in dense networks.

Inter-frequency scan (IFS) is a process performed by the user equipment (UE) to discover small cells in heterogeneous networks (HetNets) for high data rate and boosting the spectral efficiency. It is anticipated that the role of IFS will have a significant impact on the quality of service (QoS) and the energy efficiency in the future cellular systems. In this paper, a framework is presented to model and evaluate the impact of IFS on the system performance by using stochastic geometry. The energy efficiency is derived as performance metric to obtain the optimum value of the IFS rate (optimum number of scans per unit time) by taking into consideration the tradeoff between the power consumption and exploiting the abundant frequency resources at the small cells efficiently. Considering the energy consumption for performing IFSs along with the energy consumption for maintaining the uplink (UL) transmission will help to find the optimum value of IFS rate that achieves the best energy efficiency. The analysis and results show that the optimum IFS rate depends on different system parameters such as the small cells' density, UE's speed and the transmit power of the small cells (small cells coverage).

As a promising solution to offload cellular traffic, device-to-device (D2D) communication has been adopted to help disseminate contents. In this paper, the D2D offloading utility is maximized by proposing an optimal content pushing strategy based on the user interests and sharing willingness. Specifically, users are classified into groups by their interest probabilities and carry out D2D communications according to their sharing willingness. Although the formulated optimization problem is nonconvex, the optimal solution is obtained in closed-form by applying Karush-Kuhn-Tucker conditions. The theoretical and simulation results show that more contents should be pushed to the user group that is most willing to share, instead of the group that has the largest number of interested users.

High-speed railway (HSR) has witnessed a huge growth globally, and now is reaching a maximum speed of 575 km/h. This record of speed makes mobile communications difficult for HSR since the handover (HO) frequency increases which results in a high loss of connectivity. Based on distributed antenna systems (DASs), this paper utilizes the two-hop network architecture for HSR broadband wireless communication systems. With the target of achieving high system capacity, superior transmission reliability, and consequently high-quality broadband wireless communication service for passengers in HSR. Moreover, a Frequency Switch (FSW) scheme is proposed for the two-hop network architecture to alleviate the frequent HO issue in traditional HSR wireless communication systems where HO generally happens between the successive remote antenna units (RAUs) connecting to the same central unit (CU) control. The FSW scheme provides mobility robustness signalling process that guarantees a successful frequency switching instead of HO, and reduces the probability of radio link failure (RLF) compared to HO process in traditional HSR systems, where the HO failure (HOF) rate is about 21%. The analytical results show that the proposed scheme outperforms traditional HO schemes.

In this paper, optimal content caching strategy is proposed to jointly minimize the cell average outage probability and fronthaul usage in cloud radio access network (Cloud-RAN). Closed form expression of the outage probability conditioned on the user's location is presented, and the cell average outage probability is obtained through the composite Simpson's integration. The caching strategy for jointly optimizing the cell average outage probability and fronthaul usage is formulated as a weighted sum minimization problem, which is a nonlinear 0-1 integer NP-hard problem. In order to deal with the NP-hard problem, at first, two particular caching placement schemes are investigated: the most popular content (MPC) caching scheme and the proposed location-based largest content diversity (LB-LCD) caching scheme. Then a genetic algorithm (GA) based approach is proposed. Numerical results show that the performance of the proposed GA-based approach with significantly reduced computational complexity is close to the optimal performance achieved by exhaustive search based caching strategy.

In D2D-enabled cellular networks, the cellular user equipments (CUEs) and D2D user equipments (DUEs) can use the overlay in-band D2D scheme and share the channel to transmit the uplink and D2D signals. As a result, the uplink signal experiences in-band emission interference (IEI) as well as co-channel interference at the serving base station (BS). This paper demonstrates the IEI effect on the data rate of the cellular system. The results show the IEI degrades the data rate of CUEs significantly especially under high DUEs density scenario. Accordingly, we propose a density-based strategy (DBS) to mitigate IEI, and we evaluate the proposed strategy by deriving DUEs reduction percentage as a trade-off metric. The proposed strategy remarkably improves the cellular system performance.

High-speed railway (HSR) has witnessed a huge growth all over the globe reaching a maximum speed of 575 km/h. This record of speed makes mobile communications difficult for HSR since the handover (HO) frequency increases which frequently results in a loss of connectivity. This paper proposes a specialized network architecture based on distributed antenna systems (DAS) for HSR broadband wireless communication systems along with the two- hop architecture. Further, a frequency switch (FSW) scheme is proposed aiming to eliminate the need for HO between the successive small coverage remote antenna units (RAUs) that fall under the same central unit (CU) control. The analytical results show that the proposed scheme outperforms traditional HO schemes and can support the application with high quality of services (QoS) requirement.

High-speed trains have become one of the most leading transportation means, where each year the manufacture companies reach a new speed record. This progressive speed records increase the handover (HO) rate which makes it very difficult for high speed railway (HSR) mobile communication to sustain a reliable communication link. Utilizing distributed antenna systems (DASs) along with the two-hop architecture, this paper analyzes the conventional handover scheme based on this architecture and proposes a faster HO strategy. The proposed scheme reduces the HO latency and failure probability which could greatly improve the end-users quality of services (QoS). The analytical results show that the proposed scheme performs better than the conventional HO scheme.

Offloading traffic from the macro cells (MCs) with limited frequency resources to the high frequency small cells (SCs) improves the spectral efficiency and the energy efficiency in the inter-frequency heterogeneous networks (HetNets). Inter-frequency measurement (IFM) is an essential process prior to offloading terminals and their traffic to the SCs, and has a great impact on the utilization of the system resources. In this paper, we address the role of IFM in a two-tier HetNet and investigate other system parameters that impact on the offloading process. The uplink energy efficiency (UEE) is derived as a performance metric to minimize the trade-off between the power consumption at the terminals' side and exploiting the system resources. The results show that considering different number of IFM scans per unit time according to the density of SCs can improve the energy efficiency in the system significantly.

Device-to-Device (D2D) communication has been proposed as a promising way to offload traffic from the cellular network. In this paper, with the joint impact of user preference and selfishness, the D2D assisted content dissemination process is investigated in order to maximize the offloading gain via D2D communications. An alternative group pushing optimization (AGPO) algorithm is proposed to solve the formulated nonconvex problem. In addition, for the special case of two groups, the optimal solution is derived in closed-form to help validate the algorithm. Finally, the simulation results show that the AGPO algorithm converges to the global optimum and has a much lower complexity compared to exhaustive search.

In distributed massive multi-input multi-output (DM-MIMO) system, the number of users simultaneously served is greatly restricted if the pilots allocated for users are orthogonal. In this paper, a dynamic pilot reuse strategy within a single cell DM-MIMO system is proposed in order to reduce pilot overhead. The reuse in this strategy is applied so that maximum average sum-rate is satisfied within limited pilot resources. Specifically, two users in different subcells separated by large distance and meeting a specific data rate level can share the same pilot sequence. To perform this strategy, an expression for signal to interference plus noise ratio (SINR) is first derived for any pair of users who uses the same pilot. Based on this expression, an algorithm is proposed to choose which pairs of users are able to use the same pilot by comparing their potential data rates with a certain threshold. Finally, a method is employed to find the near-optimal threshold that can be utilized in the suggested algorithm to produce the maximum average sum-rate. The simulation results demonstrate that the uplink achievable sum-rate for the proposed strategy is higher than the both cases when no pilot reuse or random pilot reuse are considered.

In distributed massive multi-input multi-output (DM-MIMO) systems, orthogonal pilot sequences are generally utilized to acquire the channel state information (CSI). However, this highly restricts the number of users simultaneously served. In this paper, a pilot reuse within a single cell DM-MIMO system is proposed to serve more users than the available pilot sequences. The reuse in this strategy is applied so that maximum achievable sum-rate is satisfied with the constraint of predefined pilot resource. On this basis, two users in different subcells separated by a large distance and satisfying a specific signal to interference plus noise ratio (SINR) level can share the same pilot sequence. An expression for SINR is derived for any pair of users who use the same pilot. Based on this expression, an algorithm is proposed to choose which pairs of users are able to use the same pilot with the constraint of satisfying the minimum SINR required for these users. The simulation results demonstrate that the uplink achievable sum-rate for the proposed strategy is higher than both cases when no pilot reuse or random pilot reuse are considered.

Cloud radio access network(C-RAN) has been proposed as a novel mobile network architecture allowing centralized processing. Resource allocation (RA) is the core task in order to coordinate transmissions.In this paper efficient resource allocation technique in non-orthogonal multiple access (NOMA) based C-RAN systems is proposed. The task of RA is solved by centralized solutions in a C-RAN.The problem of maximizing the weighted sum-rate of users with target quality of service(QoS)is addressed subject to various constraints. The problem is divided into sub- problems and then sub-optimal solutions are proposed. Numerical experiments show that the proposed algorithms can improve the system performance as compared to orthogonal multiple access(OMA)-based C-RAN systems.

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability. It is more important to give high pushing probability to users with high sharing willingness.

This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), and so on. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower bound. Then, based on the successive convex approximation technique and the relationship between the data rate and the mean square error, we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the baseband unit pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

The non-orthogonal multiple access (NOMA) allows one subcarrier to be allocated to more than one user at the same time in an orthogonal frequency division multiplexing (OFDM) system. NOMA is a promising technique to provide high throughput due to frequency reuse within a cell. In this paper, a novel interference cancellation (IC) technique is proposed for asynchronous NOMA systems. The proposed IC technique exploits a triangular pattern to perform the IC from all interfering users for the desired user. The bit error rate and the capacity performance analysis of an uplink NOMA system with the proposed IC technique are presented, along with the comparison to orthogonal frequency division multiple access (OFDMA) systems. The numerical and simulation results show that the NOMA with the proposed asynchronous IC technique outperforms the OFDMA. It is also shown that employing iterative IC provides significant performance gain for NOMA and the number of required iterations depends on the modulation level and the detection method. With hard decision, two iterations are sufficient, and however, with soft decision, two iterations are enough only for low modulation level, and more iterations are desirable for high modulation level.

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l1-norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC.

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a cloud radio access network (C-RAN) where both the RRHs and users all have multiple antennas. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. We propose to solve this problem with two phases. In Phase I, a new approach is proposed to check the feasibility of the original problem. If the feasibility is guaranteed, in Phase II, a low- complexity algorithm is proposed to solve the original optimization problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side.

In this paper, a closed-form expression of the outage probability in a downlink two-tier heterogeneous network (HetNet) is derived. The second tier uses dedicated spectrum and adopts an unsynchronized time division duplex (TDD) system with a dynamic frame configuration. The marked point process is used to model both locations of transmitters (users equipment/small cell base station), in addition to the time that has passed through each small cell sub-frame at any time of a small cell sub-frame of interest. The system performance is evaluated under different traffic dominators (uplink and downlink) by taking into account the impact of the structure of the frame- configurations. Furthermore, the system parameters, such as small cell density and the uplink control factor, are also evaluated.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs), which are well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design, which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange-based algorithm in some special cases.

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.

Massive multiple-input multiple-output (MIMO) is a promising technique to greatly increase the spectral efficiency and may be adopted by the next generation mobile communication systems. Base stations (BSs) equipped with large-scale antennas can serve multiple users simultaneously by exploiting the downlink precoding in time division duplex (TDD) mode. However, channel state information (CSI) of uplink transmissions cannot be simply used for downlink precoding, because the gain mismatches of the transceiver radio frequency (RF) circuits disable the channel reciprocity. In this paper, we focus on antenna calibration for massive MIMO systems with maximal ratio transmit (MRT) precoding to solve the channel nonreciprocity problem. A new calibration method, called mutual coupling calibration, is proposed by using the effect of mutual coupling between adjacent antennas. By exploiting this method, the BS can perform the calibration without extra hardware circuit and users' involvement. We also build up the model of calibration error and derive the closed-form expressions of the ergodic sum-rates for evaluating the impact of calibration error on system performance. Simulation results verify the high calibration accuracy of the proposed method and show the significant improvement of system performance by performing antenna calibration.

In this paper, a novel interference cancellation (IC) technique is proposed for asynchronous non-orthogonal multiple access (NOMA) systems. The bit error rate (BER) performance of an uplink NOMA system with the proposed IC technique is presented. Numerical and simulation results have shown that NOMA with the proposed asynchronous IC technique outperforms NOMA with a conventional-IC technique for uplink transmissions. It is also shown that employing iterative IC provides significant performance gain in NOMA and the number of required iterations depends on the modulation level and the received power ratio between users.

The channel state information (CSI) is used to optimise data transmission in time division duplex (TDD) systems, which is obtained at the time of channel estimation. The actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation in user movement environment. In this paper the impact of different user mobility on TDD downlink multiuser distributed antenna system is investigated. Based on mobility state information (MSI), an autocorrelation based feedback interval technique is proposed and updates CSI and mitigate the performance degradation imposed by the user speed and transmission delay. Cooperative clusters are formed to maximize sum rate and a channel gain based antenna selection and user clustering based on SINR threshold is applied to reduce computational complexity. Numerical results show that the proposed scheme can provide improved sum rate over the non cooperative system and no MSI knowledge. The proposed technique has good performance for wide range of speed and suitable for future wireless communication systems.

This paper presents an indoor propagation model analysis and path loss measurements for a wireless third generation (3G) femtocell in indoor environments. An indoor line of sight (LOS) and an office-like obstructed non-LOS scenario are measured by using the femtocell which is connected to Hutchinson 3G's network to enable live signal measurements. The measurement results are compared with conventional indoor wireless models. Two well-known indoor channel models are optimized to increase the accuracy of the path-loss estimation.

We consider the downlink multi-user multi-cell massive MIMO systems, assuming that the number of antennas at base station (BS) and the number of users are large. Our system model accounts for channel estimation, pilot contamination, and uniformly random user location distribution. We derive the approximation of area spectral efficiency (ASE) with regularized zero-forcing (RZF) precoding technique which are proven to be accurate via simulation results. With a realistic power consumption model considering not only transmit power but also the fundamental power for operating the circuit at transmitter and receiver, we analyze the performance of area energy efficiency (AEE). Finally, based on the proposed power consumption model, we determine the optimal number of antennas at BS aimed at maximizing AEE when transmit power is given.

The integration of cooperative transmission into satellite networks is regarded as an effective strategy to increase the energy efficiency as well as the coverage of satellite communications. This paper investigates the performance of an amplify-and-forward (AF) hybrid satellite-terrestrial relay network (HSTRN), where the links of the two hops undergo Shadowed-Rician and Rayleigh fading distributions, respectively. By assuming that a single antenna relay is used to assist the signal transmission between the multi-antenna satellite and multi-antenna mobile terminal, and multiple interferers corrupt both the relay and destination, we first obtain the equivalent end-to-end signal-to-interference-plus-noise ratio (SINR) of the system. Then, an approximate yet very accurate closed-form expression for the ergodic capacity of the HSTRN is derived. The analytical lower bound expressions are also obtained to efficiently evaluate the outage probability (OP) and average symbol error rate (ASER) of the system. Furthermore, the asymptotic OP and ASER expressions are developed at high signal-to-noise ratio (SNR) to reveal the achievable diversity order and array gain of the considered HSTRN. Finally, simulation results are provided to validate of the analytical results, and show the impact of various parameters on the system performance.

Providing high data rate wireless transmissions has been difficult in indoor environments, particularly in multi-floor buildings. One way to achieve high data rate wireless transmissions is to reduce the radio transmission distance between the transmitter and the receiver by using distributed antenna systems (DASs) and employing frequency reuse. However, due to the reuse of the limited available spectrum, co-channel interference can severely degrade system capacity. In this paper, the uplink spectral efficiency of an in-building DAS with frequency reuse is studied, where remote antenna units (RAUs) deployed on each floor throughout the building are connected to a central unit (CU) where received signals are processed. The impact of co-channel interference on system performance is investigated by using a propagation channel model derived from multi-floor, in-building measurement results. The proposed scheme exploits the penetration loss of the signal through the floors, resulting in frequency reuse in spatially separated floors, which increases system spectral efficiency and also reduces co-channel interference. A comparative analysis with conventional co-located antenna deployment at the floor center is provided. Location based RAU selection and deployment options are investigated. System performance is evaluated in terms of location-specific spectral efficiency for a range of potential mobile terminal (MT) locations and various in-building propagation characteristics.

In this paper, we consider the problem of maximizing the energy efficiency (EE) for multiple-input-multiple-output (MIMO) interference channels (ICs), subject to the per-link power constraint. To avoid extensive information exchange among all links, the optimization problem is formulated as a noncooperative game, where each link maximizes its own EE. We show that this game always admits a Nash equilibrium (NE) and the sufficient condition for the uniqueness of the NE is derived for the case of large enough maximum transmit power constraint. To reach the NE of this game, we develop a totally distributed EE algorithm, in which each link updates its own transmit covariance matrix in a completely distributed and asynchronous way. Some players may update their solutions more frequently than others or even use the outdated interference information. The sufficient conditions that guarantee the global convergence of the proposed algorithm to the NE of the game have been given as well. We also study the impact of the circuit power consumption on the sum EE performance of the proposed algorithm in the case when the links are separated sufficiently far away. Moreover, the tradeoff between the sum EE and the sum spectral efficiency (SE) is investigated with the proposed algorithm under two special cases: 1) low transmit power constraint regime; and 2) high transmit power constraint regime. Finally, extensive simulations are conducted to evaluate the impact of various system parameters on the system performance.

In this paper, we focus on a dimmable visible light communication (VLC) system using PIN photodiodes. In such a system, the main distortion is caused by additive Gaussian noise, however, with a noise variance depending on the current signal strength. Under the non-negativity, peak power and dimmable average power constraints, the lower and upper bounds on the channel capacity are derived, respectively. Specifically, the derivation of the lower bound is based on the fact that the entropy of the output is always larger than the entropy of the input, while the derivation of the upper bound relies on the dual expression of the channel capacity and the notion of capacity-achieving input distribution that escape to infinity. Numerical results show that the gap between the lower and upper bounds is very small in the VLC application zone.

This paper aims to derive the upper and lower bounds for the channel capacity of dimmable visible light communications (VLC) systems. Because the information is modulated into the instantaneous optical intensity of light emitting diodes (LEDs), the transmitted optical intensity signal is represented by a nonnegative input, which is corrupted by an additive white Gaussian noise. Considering the illumination support and LED device ability in VLC systems, the transmitted optical intensity signal must satisfy the illumination and the allowed peak optical intensity constraints. With these constraints, a lower bound on the channel capacity is derived through the maximum mutual information between the channel input and output, which is determined by the source entropy maximization. By applying the dual expression of capacity, an upper bound on channel capacity is derived. Both the upper and lower bounds are presented as the closed forms. The numerical results show that the presented bounds are very tight. Moreover, the peak optical intensity constraints will result in the loss of channel capacity. However, such capacity loss is so small that it can be negligible when the allowed peak optical intensity is twice of the nominal optical intensity of LED devices.

We consider downlink transmission in timedivision duplexing (TDD) Massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over frequency selective fading channels. A simple multi-cell H-inf precoding method is proposed in order to achieve good suppression to pilot contamination (PC). This precoding is developed virtually by an intuitive optimization problem under H-inf performance criterion, and calculated by performing a J-spectral factorization. Based on some conditions, we obtain a simple closed-form solution to this J-spectral factorization. For a large number of base station (BS) antennas, an approximate expression of this solution could also be derived. It is shown that the approximate solution can achieve the same performance as the proposed H-inf precoding in Massive MIMO systems. Furthermore, significant performance gains compared to certain popular precoding methods are shown.

As a promising technology, Device-to-device (D2D) communication as an underlay to cellular mobile networks is proposed to improve the spectral and energy efficiency. In OFDMA cellular networks, fractional frequency reuse (FFR) is an efficient frequency reuse scheme. This paper focuses on analysing the performance of D2D communications as an underlay to the FFR based OFDMA cellular network and analytically evaluates how spectral efficiency performance is affected by parameters, such as the ratio of the transmit power for the D2D transmission to that for the cellular transmission and radius ratio of the central area to the whole cell. Also, the optimal radius ratio to achieve the highest spectral efficiency is investigated for the D2D communication underlaying to the OFDMA cellular network.

In the orthogonal frequency division multiple access (OFDMA) system, one of the efficient and low complex methods to allocate radio resources among multiple users is chunk-based resource allocation, which groups a number of adjacent subcarriers into a chunk and allocates resources chunk by chunk. In this paper, performance analysis of chunk-based resource allocation is studied in the multi-cell OFDMA environment. Fractional frequency reuse (FFR) is considered in the cellular OFDMA. Basically, FFR divides each cell into central and edge areas where two different values of the frequency reuse factor are assumed. This paper analytically evaluates how spectral efficiency performance is affected by system parameters, including radius ratio of the central area to the whole cell, transmit signal to noise ratio (SNR), number of users, number of subcarriers per chunk, and coherence bandwidth. The numerical results show that there exists an optimal radius ratio to achieve the highest spectral efficiency in the proposed research. The optimal radius ratio is about 0.7, which is almost irrespective of the SNR, number of users, and number of subcarriers per chunk. In other words, the sizes of the central area and the edge area of the whole cell are almost equal when achieving the optimal performance.

This paper aims to design an optimal set of beam-vectors for multi-band multi-antenna cognitive radio networks that jointly allocate power over both space and frequency, so that the sum power of secondary users (SUs) is minimized, subject to rate demands at the SUs, as well as the interference constraints imposed by primary users. Unlike the rate maximization problems, which are always feasible, this power minimization (PM) problem may be infeasible due to the rate constraints. Therefore, we provide a complete analysis of the PM problem by splitting the solution into two separate phases. In phase I, a novel method is developed to check the feasibility of the PM problem by considering an alternative problem, where one additional variable is introduced. This alternative problem is always feasible and one algorithm based on network duality and geometric programs is developed to solve it. In phase II, a novel algorithm is developed to solve the PM problem. This algorithm can be implemented in an online fashion. Furthermore, this algorithm is proved to converge to a Karush-Kuhn-Tucker point of the PM problem. Simulation results show that the proposed algorithms converge in only a few iterations and significantly outperform the existing single-band method in terms of both the feasibility probabilities and power savings.

The transmitted optical intensity in visible light communications (VLC) system is represented by a nonnegative input, which is corrupted by an additive white Gaussian noise. In this paper, we consider that the transmitted optical intensity signal must satisfy the illumination constraint to fulfill the illumination support in VLC system. So the average transmitted optical intensity is constrained by a target illumination intensity which is determined by the nominal optical intensity of light source devices and the dimming target. The derivation of the upper bound is based on signal space geometry via a sphere-packing argument. The lower bound is derived through the maximum mutual information between the channel input and output, which is determined by the source entropy maximization. Both the bounds are presented in terms of the closed forms. The numerical results show that the presented bounds are very tight at the application zone of dimmable VLC links.

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.

The distributed antenna system (DAS) is a promising system to provide high data rate transmissions in wireless communications. So far, most researches in the performance analysis for the DAS have been focused on single-user cases. This paper aims to investigate and present an optimal resource allocation scheme in downlink multiuser DASs. Different from the resource allocation in a conventional cellular system, the resource allocation in the multiuser DAS must consider multiple channel conditions of the transmission links between multiple remote antenna unites (RAUs) and any user, which increases the complexity of the resource allocation. In order to provide insights in the multiuser DAS, the effect of the number of RAUs used to communicate with each user is investigated extensively. In addition to power allocation, subcarrier allocation and bit allocation are studied in the orthogonal frequency division multiple access (OFDMA) DAS. To reduce the complexity of the resource allocation in the downlink multiuser DAS, the chunk allocation technique is adopted by grouping a set of contiguous subcarriers into one chunk and allocating resources chunk by chunk to users and an equivalent channel fading factor per subcarrier for a user is proposed to represent the combined channel gain from multiple RAUs to the user. The numerical results show that by using the proposed resource allocation method, each user only needs to be connected to a few RAUs, depending on the location of the user.

Over the last few years high data rate wireless transmission has gained considerable attention in hot spot areas, including high buildings. It has been demonstrated that distributed antenna systems (DASs) improve the performance of the indoor environment by covering dead spots. In this paper, the achievable rate is investigated for indoor high data rate wireless transmission in high buildings when DASs are employed. In the system, radio transmission/reception within one or several neighbouring floors is controlled by one central unit (CU) exploiting the entire system bandwidth. In order to efficiently use the spectrum, the same frequency channels are reused among floors controlled by different CUs. The radio signals in high buildings can propagate vertically and reach the neighbouring floors. Therefore, the performance of the system is limited by cochannel interference. Direct propagation inside the building and reflection from nearby buildings are considered in the channel model. The achievable rate of the system is evaluated through analysis and simulation. The impact of representative system parameters on the achievable rate, including the number of remote antenna units (RAUs), the frequency reuse factor, the floor penetration loss, the path loss exponent, and the Nakagami fading parameter, is discussed.

This paper presents a novel scheduling policy for wireless communications where users contain mixed real-time (RT) and non-real-time (nRT) traffic. The proposed policy introduces a novel tradeoff concept in scheduling, where a value of quality of service (QoS) satisfaction can be traded off with the achievable system throughput. Two weighting factors, corresponding to RT and nRT traffic, are introduced in the novel scheduling policy. The proposed policy is evaluated by simulation results. The simulation results show an optimal range of the weights to meet QoS satisfactions.

In this paper, the error rate performance and diversity gain of amplify-and-forward (AF) wireless relay networks are studied. The distributed space-time block code (DSTBC) in a synchronous relay network with multiple relay nodes is considered. The distributed linear dispersion code is used at the relays. The symbol (or codeword) error rate is studied in terms of upper and lower bounds through rigorous derivations, and the diversity gain of the DSTBC is derived, for any number of relay nodes. As a corollary, a necessary and sufficient condition is obtained for achieving full cooperative diversity. Arbitrary coding matrices at the relays, path loss and general linear power allocation are considered.

In OFDMA systems, chunk-based resource allocation is an effective approach to reduce the complexity of resource allocation by grouping a number of adjacent subcarriers into a chunk and allocating resources chunk by chunk. In this paper, chunk-based resource allocation is investigated in a multi-cell environment. To improve the spectral efficiency in the multi-cell system, fractional frequency reuse (FFR) is also adopted in the chunk-based OFDMA system. After theoretically analyzing the spectral efficiency of the chunk-based OFDMA system with FFR, the optimal value of the radius ratio, which is the ratio of the radius of the central area to the radius of the cell, to achieve the optimum system performance is given by numerical simulations. Numerical results show that the average spectral efficiency increases with increasing the number of users in the system. However, the value of the radius ratio to achieve the largest spectral efficiency is not affected by the number of users or the number of subcarriers per chunk.

This paper presents a behavioral fairness-based resource allocation (RA) algorithm for uplink transmission of a multiuser orthogonal frequency division multiplexing system. The aim of fairness- based RA is to boost users' willingness to start and keep cooperating with each other and behave well to keep their trust and reputation high. An interesting notion of differentiated streams is also included in the proposed RA, which can fine-tune RA result to transfer excess bandwidth and power from real-time streams to non-real-time streams, i.e. increase useful throughput. In order to reduce the complexity of RA, a set of contiguous subchannels are grouped into a chunk to allocate resource chunk by chunk. The effects of users' contribution and the number of subchannels grouped in a chunk on the average throughput is analyzed and shown by numerical simulations.

With the deployment of high speed train (HST) systems increasing worldwide and their popularity with travelers growing, providing broadband wireless communications (BWC) in HSTs is becoming crucial. In this paper, a tutorial is presented on recent research into BWC provision for HSTs. The basic HST BWC network architecture is described. Two potential cellular architectures, microcells and distributed antenna systems (DASs) based cells, are introduced. In particular, the DAS is discussed in conjunction with radio over fiber (RoF) technology for BWC for HSTs. The technical challenges in providing DAS-based BWC for HSTs, such as handoff and RoF are discussed and outlined.

In this paper, the error rate performance and diversity gain of amplify-and-forward (AF) wireless relay networks are studied. The distributed space-time block code (DSTBC) in a synchronous relay network with multiple relay nodes is considered. The distributed linear dispersion code is used at the relays. The symbol (or codeword) error rate is studied in terms of upper and lower bounds through rigorous derivations, and the diversity gain of the DSTBC is derived, for any number of relay nodes. As a corollary, a necessary and sufficient condition is obtained for achieving full cooperative diversity. Arbitrary coding matrices at the relays, path loss and general linear power allocation are considered. Finally, the orthogonal frequency division multiplexing (OFDM)-based space-time transmission scheme in asynchronous cooperative networks is also discussed.

By grouping a number of adjacent subcarriers into a chunk, resource allocation can be carried out chunk by chunk in orthogonal frequency division multiple access (OFDMA) systems. Chunk-based resource allocation is an effective approach to reduce the complexity of resource allocation in OFDMA systems. In this paper, a chunk-based resource allocation scheme, i.e. joint chunk, power and bit allocation, is proposed and analyzed by maximizing the throughput under a total transmit power constraint. A scaling factor is introduced to achieve optimal allocation. Considering the digital nature of bits per symbol per subcarrier (bits/symbol/subcarrier), a digitization process is proposed to digitize the theoretically allocated bits/symbol/subcarrier to integer. System parameters, such as the power constraint, number of users, coherence bandwidth, number of subcarriers and number of chunks, are introduced and their impacts on the average throughput are studied. The performance of the dynamic power allocation scheme is compared with the fixed power allocation scheme. The numerical results show that the theoretical throughput of the fixed power allocation scheme is quite close to that of the dynamic power allocation scheme. However, when the digital nature of bits/symbol/subcarrier is considered, the average throughput of the dynamic power allocation outperforms the fixed power allocation scheme.

Over the last few years high data rate wireless transmission has gained considerable attention in hot spot areas, including high buildings. It has been demonstrated that distributed antenna systems (DASs) improve the performance of the indoor environment by covering dead spots. In this paper, the DAS is investigated in high building to provide high data rates for indoor wireless mobile communications by exploiting spatial diversity and reducing radio transmission distance. In the proposed indoor DAS, several neighbouring floors compose a floor-bank and are controlled by one central unit (CU). The spectrum is not reused in a floor-bank, but can be reused among different floor-banks, which causes co-channel interference. In order to analyse the performance of the indoor DAS in high building with the presence of co-channel interference, an accurate propagation model of the interference from other floor is a key requirement. Direct propagation inside the building and reflection from nearby buildings have been considered in the channel model. Based on the theoretical analysis, the impact of several system parameters on the performance is presented in terms of BER. Numerical results indicate that the position of the user in the floor has significant effect on the system performance and the attenuation introduced by the floor separation in high buildings should be taken into consideration during the planning of the number of floor in each floor-bank.

The microcellular system and distributed antenna system (DAS) are two promising systems to provide high data rate for future wireless communications, especially for the cell edge of the macrocell, because both systems can reduce the radio transmission distance. The cell edge spectrum efficiency is theoretically compared between the two systems in a network topology with seven macrocells, each of which has seven hexagonal sectors. Numerical results show that, when the spatial diversity and spectrum reuse are adopted in the DAS, the cell edge spectrum efficiencies per sector in the DAS is larger than these in the microcellular system.

In wireless systems, coverage and capacity is a major challenge especially in high buildings. The need to reuse the limited available spectrum results in cochannel interference. Cochannel interference is a very critical factor affecting system performance. It limits the uplink coverage and capacity of wireless networks such as femtocells. Joint processing of the received signal among cochannel receivers has recently been shown to be very beneficial from an interference cancellation point of view. This paper proposes a femtocell based distributed antenna system (DAS) for uplink transmission in high buildings where distributed antenna units deployed on each floor of the building are connected to femto base stations (BS). The femto BSs can cooperate through joint signal processing for all cells in order to estimate the received signal, reduce cochannel interference introduced by frequency reuse among the femtocells and maintain high spectral efficiency. Analysis and numerical results show remarkable gains especially when interference cancellation is employed at each cooperative femto BS, leading to many more simultaneous users being supported on the same frequency and resulting in large capacity increase when compared with the traditional system with no interference cancellation technique.

In orthogonal frequency division multiple access (OFDMA) systems, efficient resource allocation is very important to improve the performance of the OFDMA system. In this paper, the resource allocation scheme is investigated by considering different criterions to packets on first transmissions and on retransmissions. Under a bit error rate (BER) constraint, the objective is to maximize the throughput of packets on first transmissions, meanwhile decreasing the longest packet delay caused by retransmitted packets and guaranteeing all packets on retransmissions to be sent out. A low-complexity suboptimal algorithm that separates allocation for packets on retransmissions and on first transmissions is proposed. Adaptive modulation scheme is adopted to guarantee the BER constraint, except the modulation for each retransmitted packet remains the same. In order to improve retransmission liability, the retransmitted packets are combined with its failed packets in previous transmission.

This paper is focused on the study of layered space-time-frequency (LSTF) architectures with channel coding for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing systems for high speed wireless communications over a frequency-selective fading channel. In order to achieve the available spatial, temporal and frequency diversities, and also make the system implementation feasible for high speed OFDM MIMO multiplexing, a novel LSTF architecture with multiple channel encoders is proposed with each independently coded layer being threaded in the three-dimensional space-time-frequency transmission resource array. Non-iterative receiver is adopted which consists of list sphere detector and irregular low-density parity-check (LDPC) codes as the constituent codes. Simulation results show that the performance of the proposed multiple-encoder LSTF architecture is very close to that of the conventional single-encoder LSTF where coding is applied across the whole information stream. However, due to the use of multiple parallel encoders/decoders with a shorter codeword length, the proposed LSTF architecture has much lower hardware processing speed and complexity than the conventional LSTF.

In orthogonal frequency division multiple access (OFDMA) systems, efficient resource allocation is very important to improve the performance of the OFDMA system. The general principle of resource allocation in an OFDMA system is to assign each subcarrier to the user with the best channel condition for that subcarrier. However, in delay tolerant traffic, current work did not consider that packets on retransmissions may have different allocation criteria from those on their first transmissions. In this paper, the resource allocation scheme is investigated by considering different criterions to packets on first transmissions and on retransmissions. Under a bit error rate (BER) constraint, the objective is to maximize the throughput of packets on first transmissions, meanwhile decreasing the longest packet delay caused by retransmitted packets and guaranteeing all packets on retransmissions to be sent out. A low-complexity suboptimal algorithm that separates allocation for packets on retransmissions and on first transmissions is proposed. Adaptive modulation scheme is adopted to guarantee the BER constraint, except the modulation for each retransmitted packet remains the same. In order to improve retransmission liability, the retransmitted packets are combined with its failed packets in previous transmission.

Over the last decade high data rate wireless transmission has gained considerable attention in hot spot areas, including high buildings. It has been demonstrated that distributed antenna systems (DASs) improve the performance of the indoor environment by providing spatial diversity and covering dead spots. In this paper, indoor wireless communications have been studied together with a femtocell based DAS. In this DAS, a femtocell managed by a central unit (CU) is deployed to cover several neighbouring floors, which compose a floor-bank. Since the radio spectrum becomes limited and expensive, the spectrum should be reused among different floor-banks. The performance of downlink DAS transmission is analysed in terms of bit error rate (BER) in the presence of co-channel interference from other floor-banks. Numerical results show that when decreasing the number of floors in a floor-bank, the BER performance degrades due to the strong co-channel interference. The number of floors in a floor-bank has a significant effect on the BER performance in high buildings.

As the traffic grows rapidly in wireless communication, the spectrum becomes scarce. The current inefficient fixed spectrum allocation schemes will not satisfy the growing spectrum demand. This paper proposes a double auction based dynamic spectrum sharing scheme for multiple operators adopting OFDMA in the downlink. More specifically, this scheme allows free subcarriers trading between operators to improve the efficiency of the spectrum utilization. In contrast with conventional auction, double auction deals with a “multiple seller and multiple buyer” case, which is more general in inter-operator spectrum sharing. In the proposed scheme, the sellers and buyers estimate the value of the subcarriers first. Then they submit their asks (bids) to the auctioneer based on their valuations. Bidding and asking strategy in a practical framework is proposed as well. Finally, two double auction methods are examined in the simulation.

Chunk-based resource allocation can effectively reduce the complexity of resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) systems by grouping a number of neighboring subcarriers into a chunk and being carried out chunk by chunk. To ensure the bit error rate (BER) constraint within a chunk, an adaptive BER-based chunk allocation scheme is proposed and analyzed in this paper with the objective of maximizing the average throughput under a limited total transmit power. Numerical results illustrate that under the same power constraint, the average throughput of the adaptive chunk allocation is very close to that of the subcarrier-based allocation in the multiuser OFDM system.

The 20 papers in this special issue on wireless video transmissions are divided into four categories: wireless video streaming, optimization and scheduling; wireless video broadcast/multicast; retransmission techniques; and video quality assessment.

This paper analyzes throughput performance of the chunk-based subcarrier allocation by considering the average bit-error-rate (BER) constraint over a chunk in downlink multiuser orthogonal frequency division multiplexing (OFDM) transmission. The outage probabilities per subcarrier are compared between the average BER-constraint based chunk allocation and the average signal-to-noise-ratio (SNR) based chunk allocation. The effects of system parameters, including the coherence bandwidth, the number of subcarriers per chunk, and the number of users, are evaluated. The numerical results show that, when the chunk bandwidth is smaller than the coherence bandwidth, the average downlink throughput of the chunk-based subcarrier allocation is very close to that of the single-subcarrier-based allocation. The effective throughput of the average BER-constraint based chunk allocation is higher than that of the average SNR based chunk allocation, especially when the number of users is large.

With the rapid development of wireless Internet services, several WLAN service providers may coexist in one public hotspot to compete for the same group of customers, leading to an inevitable price competition. The charged price and the provisioned packet loss at each provider are major factors in determining users' demands and behaviors, which in turn will affect providers' revenue and social welfare. In this paper, we set up a novel game model to analyze a duopoly price competition. We first show the users' demands are distributed between providers according to a Wardrop equilibrium and then prove the existence of a Nash equilibrium on providers' charged prices. Through analysis, we further find that in Nash equilibrium state the social welfare is very close to its maximal value in cooperative situation. Furthermore, the providers' aggregate revenues also do not decrease when the users have high sensitivity about the charged prices. Thus the competitive duopoly WLAN market can still run in an efficient way even in the absence of complex regulation schemes.

We study the problem of achieving proportional fair resource allocation in a non-cooperative multiuser OFDM network. We propose an auction-based scheduling algorithm, which combines the merits of the VCG auction and the greedy MC PF algorithm, to ensure that wireless users truthfully declare their resource requirements even though the users are inherently selfish. Through simulations, we find that users lying about their resource requirements are severely penalized by very high payments so that they should rather declare true valuations of subcarriers to the scheduler. Thus, the proposed auction-based scheduling algorithm can be used efficiently in a non-cooperative situation to realize proportional fairness.

In this paper, an channel and queue aware fair (CQAF) packet scheduling scheme is proposed for the downlink packet transmission in multiuser orthogonal frequency division multiplexing (OFDM) systems. By making use of the information on the channel conditions and the queue lengths, the proposed CQAF packet scheduling scheme efficiently allocates the subcarriers, transmission power and modulation level to users under the constraints of total transmission power, the number of subcarriers, bit-error-rate (BER) requirement and generalized processor sharing (GPS)-based fairness requirement. The numerical results show that the proposed CQAF packet scheduling scheme can reduce the transmission delay and queue length significantly while maximizing system throughput and maintaining fairness among users.

Motivated by the increasing popularity of multicarrier code division multiple access (MC-CDMA) systems, this letter studies the throughput maximization of MC-CDMA as a downlink multi-carrier multiple-access scheme and presents the theoretical analysis for the optimal solution to throughput optimization problem of downlink MC-CDMA systems. Then, a low-complexity allocation method is proposed with negligible throughput loss. Finally, several resource allocation principles are presented.

This letter addresses the problem of symbol error probability (SEP) analysis for maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) multiplexing systems. A new analytical method is presented based on the total probability theorem. The effects of imperfect channel estimation and power allocation scheme are investigated. The accuracy of the proposed method is demonstrated by Monte-Carlo simulations. It is shown that the analytical results match quite well with the simulation ones irrespective of the signal-to noise- ratio (SNR).

In this paper, throughput performance analysis of the chunk-based subcarrier allocation is presented by considering the average bit-error-rate (BER) constraint over a chunk in downlink multiuser orthogonal frequency division multiplexing (OFDM) transmission. The outage probabilities per subcarrier are compared between the average BER-constraint based chunk allocation and the average signal-to-noise-ratio (SNR) based chunk allocation. The effects of system parameters, such as the number of users, the number of subcarriers per chunk, and the coherence bandwidth, are evaluated. The numerical results show that, when the chunk bandwidth is smaller than the coherence bandwidth, the average downlink throughput of the chunk-based subcarrier allocation is very close to that of the single-subcarrier-based allocation. When the number of users is small, the average throughput increases dramatically with increasing the number of users due to multiuser diversity, whereas when the number of users is large, the multiuser diversity gain is saturated. The effective throughput of the average BER-constraint based chunk allocation is higher than that of the average SNR based chunk allocation, especially when the number of users is large or when the ratio of the chunk bandwidth to the coherence bandwidth is large.

This paper investigates the effects of channel estimation errors on zero-forcing (ZF) vertical Bell laboratories layered space time (V-BLAST) detection. An analytical method is presented to derive the symbol error probability (SEP) of the signals detected at each stage. The effects of imperfect channel estimation on the SEP performance of V-BLAST detection are then studied. It is shown that ZF-VBLAST detection is very sensitive to the channel estimation errors under high signal to noise ratio (SNR). It is also shown that when optimal ordering is adopted, the effects of channel estimation errors are more significant on the latter detection stages.

In many practical scenarios, wireless devices are autonomous and thus, may exhibit non-cooperative behaviors due to self-interests. For instance, a wireless user may report bogus channel information to gain resource allocation advantages. Such non-cooperative behaviors are practicable as the device's software could be modified by the user. In this paper, we first analyze the impact of these rationally selfish behaviors on the performance of packet scheduling algorithms in time-slotted wireless networks. Using a mixed strategy game theoretic model, we show that the traditional Maximum Rate packet scheduling algorithm can lead non-cooperative users to undesirable Nash equilibriums, in which the wireless channels are used inefficiently. By using repeated game to enforce cooperation, we further propose a novel game theoretic approach that can lead to an efficient equilibrium.

In this paper, accurate approximations of the spectral efficiency are presented for co-located multiple-input multiple-output (C-MIMO) and distributed MIMO (D-MIMO) cellular systems in the composite channel model, which includes path loss, shadow fading, and multipath fading. Firstly, conditioned on the desired user position, the analytical approximations of the mean and variance of the mutual information are derived for C-MIMO and D-MIMO at high SNR. Because the exact distribution of the mutual information in a composite Rayleigh- lognormal environment is difficult to analyze, we apply Gaussian approximation to the distribution of the mutual information. Then, the growth in ergodic capacity and outage capacity of a D-MIMO channel with the number of antennas as well as the variance of the shadow fading is well understood. Assuming that the users are randomly distributed in the cell, the closed-form expressions for the mean spectral efficiency and mean outage spectral efficiency are derived. Finally, the numerical results are presented which validate the analytical results.

This paper addresses the problem of performance analysis for maximum likelihood (ML) detection in two-input multiple-output multiplexing systems. A novel analytical method is presented to formulate the symbol error probability (SEP). Based on the total probability theory, the SEPs of the two transmitted signals are obtained in closed-form by solving the SEP equations. Both equal and unequal power allocations are investigated. The accuracy of the proposed method is verified by Monte-Carlo simulations. The proposed method can also be extended to systems with more than two inputs.

In this paper, an analytical approach to performance analysis of vertical Bell laboratories layered space time (V-BLAST) detection with optimal ordering for systems with two transmit antennas is presented. The post-detection signal-to-noise-ratio (SNR) at each stage is derived and the symbol error probabilities (SEP) of the signals are then given in closed-form. The analysis takes into account the effects of optimal ordering, imperfect channel estimation and error propagation, which were rarely considered in the literature due to the difficulties in evaluation. The accuracy of the analysis is demonstrated by Monte-Carlo simulations.

In multicarrier code division multiple access (MC-CDMA), a good bit error rate (BER) performance can be achieved by using frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion. However, the presence of residual inter-code interference (ICI) after MMSE-FDE and despreading degrades the BER performance of the multi-code MC-CDMA. The use of ICI cancellation can improve the BER performance of the multi-code MC-CDMA. To further improve the BER performance, transmit diversity technique is effective. In this paper, cyclic delay transmit diversity (CDTD) is considered. CDTD can achieve the frequency diversity gain, whereas the well-known space-time transmit diversity (STTD) can achieve the antenna diversity gain owing to the space-time coding and provide a better BER performance than CDTD. It is shown in this paper that the performance degradation of CDTD is mainly due to the residual ICI after MMSE-FDE. In order to achieve a good BER performance, the multi-code MC-CDMA using joint CDTD and ICI cancellation is proposed, and its BER performance is evaluated by computer simulation.

This letter investigates the effect of Doppler frequency shift on the performance of MMSE receivers for high speed variable spreading factor (VSF) orthogonal frequency and code division multiplexing (OFCDM) systems in broadband wireless communications. In VSF-OFCDM systems, the existence of multicode interference (MCI) resulting from multiple code channels occupying the same bandwidth degrades the performance. A full MMSE receiver is analytically studied in this letter, exploiting correlation properties among transmitted data on different subcarriers to combat the MCI. Furthermore, considering the fact that the fast time-varying fading destroys the orthogonality of the time domain spreading codes and thus introduces time domain MCI, its effect on system performance is evaluated accurately. It is also shown that it is expected to use time domain spreading factor NTof 8 for a large range of Doppler frequencies, and frequency domain spreading factor as large as possible, especially in high signal to noise ratio. Moreover, in order to improve performance, the pilot channel should occupy around 20% to 25% of the total transmitted power for a large range of Doppler.

To provide high speed mobile services, orthogonal multi-code transmission is employed in the orthogonal frequency and code division multiplexing (OFCDM) system. However, in a wireless frequency selective channel either with or without Doppler shift, code orthogonality is distorted and multi-code interference (MCI) is caused. The variance of the MCI is dependent on which codes to be used. This paper investigates the code assignment in the OFCDM system, aiming to mitigate the MCI. The code assignment can be explored in terms of the code distance defined in this paper. Generally, the codes with large code distances to each other should be assigned with high priority. Moreover, to avoid large interference from the powerful code-multiplexed pilot channel, the time spreading code adjacent to the pilot code in the code tree (Adachi et al., 1998) is suggested not be used when the time domain spreading factor is large. The performance of the proposed non-sequential code assignment scheme is analytically evaluated. It has been shown that the system performance can be improved significantly by the non-sequential scheme under various channel conditions, especially when a low to medium system load is assumed.

OFDM systems are the major cellular platforms for supporting ubiquitous high performance mobile applications. However, there remain a number of research challenges to be tackled. One of the most important challenges is the design of a judicious packet scheduler so as to make efficient use of the spectrum bandwidth. In this paper, we propose a new QoS-aware proportional fairness (QPF) packet scheduling policy for the downlink of multiuser OFDM systems to allocate radio resource among users. Our proposed algorithm is based on a cross layer design in that the scheduler is aware of both channel and queue state information to achieve proportional fairness while improving each user's QoS performance. Simulation results indicate that the proposed QPF algorithm is efficient in terms of average system throughput, packet dropping probability, and packet delay, while maintaining adequate fairness among users.

Ultra wideband (UWB) systems are currently an important wireless infrastructure for efficient short- range communications. To improve the system efficiency while guaranteeing the radio link level quality of services, transmission rate and power of the mobile nodes can be dynamically adjusted by executing an optimization algorithm at the access points (APs). In this paper, we present a cross layer rate and power allocation algorithm based on the multilayer model of time hopping (TH) pulse position modulation (PPM) UWB multimedia networks. We consider the performance of the TCP protocol under the proposed cross layer allocation scheme in various realistic UWB based infrastructure networking scenarios.

As a wireless access technique for future broadband mobile communications, orthogonal frequency and code division multiplexing (OFCDM) systems employ multi-code transmission to provide high speed mobile services. Since the multiple code channels are no longer orthogonal in the broadband wireless channel, multi-code interference (MCI) is caused. The variance of the MCI is dependent on which codes to be used. This paper investigates the code assignment in the multi-code OFCDM system, aiming to mitigate the MCI at the receiver. Since two-dimensional (2-D) spreading is employed in the OFCDM system, the code assignment is carried out in time and frequency domains, separately. In order to avoid large interference from the powerful code- multiplexed pilot channel, the time spreading code adjacent to the pilot code in the code tree is suggested not be used when the time domain spreading factor is large. The performance of the proposed code assignment scheme is analytically evaluated. It has been shown that the system performance can be improved significantly by the proposed scheme under various channel conditions, especially when a low to medium system load is assumed.

For orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing in high speed wireless transmission under broadband fading channel, a novel coded layered space-time-frequency (LSTF) architecture is proposed, which can achieve the available spatial, temporal and frequency diversity, and make the system implementation easy for high data rate transmission. In this novel architecture, each independently coded layer is threaded in the three-dimensional space-time-frequency transmission resource array. Channel estimation based on a time-multiplexed pilot channel is employed. The convolutional code is used as the constituent code, and the iterative receiver structure consisting of a linear minimum-mean-square-error soft-interference- cancellation (LMMSE-SIC) detector and the soft-input soft-output (SISO) maximum a posteriori (MAP) decoders is adopted. Simulation results show that the proposed LSTF architecture can considerably outperform the LSTF (i.e., LSTF-b) in which each independently coded layer is associated with a permanently assigned antenna,, and get almost the same performance as the LSTF (i.e., LSTF-a) where coding is applied across the whole information stream. Since its structure consists of multiple parallel lower-speed encoders/decoders with shorter codeword length, the proposed LSTF architecture can be much more easily implemented than the LSTF-a. Moreover, four iterations for joint detection and decoders are sufficient, and the percentage of the pilot channel energy in one packet should be in the range of [16%, 24%] to provide near optimum performance.

In many practical scenarios, wireless devices are autonomous and thus, may exhibit non-cooperative behaviors due to self interests. For instance, a wireless user may report bogus channel information in order to gain resource allocation advantages. In this paper, we analyzed the impact of these rationally selfish and non-cooperative behaviors on the performance of packet scheduling algorithms in time-slotted wireless networks. Using a mixed strategy game theoretic model, we found that the traditional rate maximizing packet scheduling algorithms can lead non-cooperative devices to undesirable Nash equilibria, in which the wireless channel is used inefficiently. Motivated by this observation, we proposed a novel game theoretic scheduling approach that can lead to more efficient equilibria where all competing devices can achieve higher rates.

Ultra wideband impulse radio is a promising radio technology for networks delivering extremely high data rates at short ranges. The use of extremely short duration pulses however makes synchronization task more difficult. In this paper a two-stage acquisition with serial search noncoherent correlator for time hopping impulse radio is proposed. The proposed two-stage acquisition scheme gets chip timing synchronization, and aligns phase of the local time-hopping code in two stages successively. With the aid of flow-graph approach, the analytical expressions are presented for the mean acquisition time and the probability of acquisition. Numerical results in a slow fading channel show that the proposed two-stage acquisition method can offer much shorter mean acquisition time or much higher probability of acquisition than that of the conventional acquisition.

For high speed orthogonal frequency division multiplexing (OFDM) plus multiple-input multiple-output (MIMO) multiplexing, a new coded layered space-time-frequency (LSTF) architecture with iterative signal processing at the receiver is proposed, where each independent codeword is threaded in the three-dimensional (3D) space-time-frequency transmission resource array. The iterative receiver structure is adopted consisting of a joint minimum-mean-square-error soft-interference-cancellation (MMSE-SIC) detector and the maximum a posteriori (MAP) convolutional decoders. Simulation results show that the proposed LSTF architecture can achieve almost the same performance as the LSTF (i.e., LSTF-a) where coding is applied across the whole information stream. However, due to its structure of multiple parallel lower speed encoders/decoders with shorter codeword length, the proposed LSTF architecture can be more easily implemented than the LSTF-a.

This paper investigates the performance of turbo-coded orthogonal frequency and code division multiplexing (OFCDM) systems with soft multi-code interference (MCI) cancellation. In order to regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft decision functions are proposed, i.e., LLR-soft-decision and Gaussian-soft-decision functions. By means of computer simulations, it is shown that in a highly frequency selective channel, soft MCI cancellation and MMSE detection can significantly improve the performance of turbo-coded OFCDM systems. Two iterations in turbo decoding are sufficient for both hard and soft decision functions. The proposed soft decision functions outperform the hard decision function with various channel conditions and system parameters.

Ultra wideband (UWB) systems are considered as the key wireless infrastructure technology for efficient short-range communications and mobile applications. In this paper, we study the problem of bandwidth scheduling in a UWB based hierarchical wireless ad hoc network, which is typically used in an enterprise-scale mobile computing environment. A novel self-organized clustering method is designed to improve system throughput. Simulation results suggest that the proposed clustering method is effective under various system configurations

This paper is focused on the study of layered space-time-frequency (LSTF) architectures with channel coding for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing system in high speed wireless transmission under frequency-selective fading channel. In order to achieve the available spatial, temporal and frequency diversity, and at the same time make the system implementation feasible for ultra high data rate OFDM MIMO multiplexing, we propose a new LSTF architecture with each independently coded layer being threaded in the three-dimensional space-time-frequency transmission array. List sphere decoding is used for MIMO multiplexing detection, and the irregular low-density parity-check code (LDPC) is chosen as the channel code. Three construction rules are adopted to generate high coding rate irregular LDPC codes with low encoding complexity and little performance loss. Simulation results show that the proposed LSTF architecture can significantly outperform the LSTF assuming each independently coded layer being transmitted through a permanently assigned antenna, and get almost the same performance as the LSTF applying coding across the whole information stream. On the other hand, due to its structure of multiple parallel lower-speed codecs with shorter codeword length, the proposed LSTF architecture is more realizable for ultra high data rate transmission than the LSTF applying coding across the whole information stream

In this paper, the performance of turbo-coded orthogonal frequency and code division multiplexing (OFCDM) systems is investigated with soft multi-code interference (MCI) cancellation. To regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft decision functions are proposed, called LLR-soft-decision and Gaussian-soft-decision functions. By means of computer simulations, the performance of soft MCI cancellation is studied extensively and compared to that of hard ones. It is shown that in a highly frequency selective channel, the proposed soft decision functions outperform the hard decision function with various channel conditions and system parameters. Furthermore, the Gaussian-soft-decision function provides better performance than the LLR-soft-decision function

In this paper, the performance of turbo-coded orthogonal frequency and code-division multiplexing (OFCDM) systems is investigated with soft multicode interference (MCI) cancellation and minimum mean-square error (MMSE) detection for downlink transmission in future high-speed wireless communications. To regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log-likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft-decision functions are proposed, called LLR-soft-decision and Gaussian-soft-decision functions. The Gaussian assumptions used for deriving these two soft functions are verified by simulation results, and simple methods are proposed to estimate parameters used in the soft functions in practical systems. By means of computer simulations, the performance of soft MCI cancellation is studied extensively and compared with that of hard ones. It is shown that in a highly frequency-selective channel, soft MCI cancellation and MMSE detection can significantly improve the performance of turbo-coded OFCDM systems. Two iterations in turbo decoding are sufficient for both hard and soft-decision functions. The proposed soft-decision functions outperform the hard-decision function with various channel conditions and system parameters, such as the channel correlation, the quality of channel estimation, the number of iterations in turbo decoding and the frequency-domain spreading factor (N/sub F/). Furthermore, the Gaussian-soft-decision function provides better performance than the LLR-soft-decision function. Finally, although frequency diversity gain is saturated for large channel correlation when N/sub F/ is large as in , the gain increases further with increasing N/sub F/ for small channel correlation even when N/sub F/ is large.

Orthogonal frequency and code-division multiplexing (OFCDM) systems have been introduced for high-speed data transmission in future wireless mobile communications. In this paper, a hybrid multicode interference cancellation (MCI) and minimum mean-square error (MMSE) detection scheme is presented for the turbo-coded OFCDM systems. Channel estimation based on a code-multiplexed pilot channel is employed. The weights of the hybrid detection are derived theoretically and an effective method to generate the weights in practical applications is proposed. By means of computer simulation, the effects of system parameters on the performance are studied extensively. It is shown that the hybrid detection outperforms pure MMSE detection in various channel conditions, especially for high-level modulation schemes. To carry out interference regeneration for the hybrid detection, the conventional turbo decoding algorithm which only decodes systematic bits should be extended to decode parity bits as well. Moreover, two iterations in turbo decoding are sufficient to provide good performance for the system with the multistage hybrid detection. Finally, given time-domain spreading factor N/sub T/, the system performance improves with frequency-domain spreading factor N/sub F/. However, the frequency diversity gain is saturated when N/sub F/ is large (i.e., N/sub F//spl ges/16).

Ultra-wideband (UWB) technology has been considered an innovative solution for future short-range high-speed wireless communications. Interference suppression is important for the UWB devices to operate over spectrum occupied by narrowband systems. In this paper, the use of a notch filter in time-hopping impulse radio (TH-IR) for UWB communication is considered, where a Gaussian monopulse is employed with pulse position modulation. Lognormal channel fading is assumed, and a complete analytical framework is provided for the performance evaluation of using a transversal-type notch filter to reject narrowband interference (NBI). A closed-form expression of bit-error probability is derived, and the numerical results show that the use of a notch filter can improve the system performance significantly. Furthermore, a performance comparison between TH-IR and multicarrier code-division multiple-access (MC-CDMA) UWB systems is made under the conditions of the same transmit power, the same data rate, and the same bandwidth. It is shown that in the presence of NBI, the TH-IR system and MC-CDMA system achieve similar performance when both use a notch filter.

Hybrid automatic-repeat-request (ARQ) is a flexible and efficient technique for data transmissions. In hybrid ARQ, subpacket schemes are more attractive for systems with burst errors than complete packet schemes. Although subpacket schemes were proposed in ARQ systems, optimum subpacket transmission is more effective to maximize throughput in a dynamic channel. Since convolutional codes have properties of burst errors in decoding, the optimum subpacket can be applied to convolutional codes. This paper investigates the performance of subpacket transmission for convolutionally coded systems. An efficient method is proposed to estimate the optimum number of subpackets, and adaptive subpacket schemes, i.e., schemes that enable a system to employ different optimum numbers of subpackets under various conditions, are suggested to achieve the maximum throughput of the system. Numerical and simulation results show that the adaptive subpacket scheme is very effective for the convolutionally coded hybrid ARQ system, and it can provide higher throughput, smaller delay,and lower dropping rate than complete packet schemes. Moreover, the adaptive subpacket scheme can be flexibly used with packet combining techniques to further improve the system throughput.

The orthogonal frequency and code division multiplexing (OFCDM) system with 2-D spreading (time- and frequency-domain spreading) is becoming a promising candidate for future broadband wireless communication systems. OFCDM is more attractive than orthogonal frequency-division multiplexing (OFDM) both by introducing frequency-domain spreading for frequency diversity provision and time-domain spreading for flexible data rate provision. To provide high-speed mobile services, multicode transmission is employed in conjunction with OFCDM. In a Gaussian or flat-fading channel, multicode channels are orthogonal. However, in a realistic wireless channel, the orthogonality no longer maintains. Thus, multicode interference (MCI) is caused. This paper focuses on the investigation of the effect of Doppler shift on the downlink transmission of high-speed mobile OFCDM systems. A practical channel estimation algorithm based on a code-multiplexed pilot channel is employed to track the variations of fading channels. Hybrid MCI cancellation and minimum mean-square error (MMSE) detection proposed by the authors is employed as an efficient way to eliminate the MCI in the frequency domain. The system performance is analytically studied with imperfect channel estimation to show how it is affected by parameters such as the window size in the channel estimation, Doppler shift, the number of stages of the hybrid detection, the power ratio of pilot to data channels, spreading factor, and so on

This paper studies the effect of channel estimation error and antenna diversity on multilevel quadrature amplitude modulation (M-QAM) systems over Rayleigh fading channels. Based on the characteristic function method, a general closed-form bit-error rate (BER) for M-QAM systems is presented. The effect of the inaccurate channel estimation on the performance for pilot-symbol-assisted modulation M-QAM systems with antenna diversity is investigated. Simulation results for M-QAM (M = 4, 16, 64, 256, etc.) show that the analytical method can accurately estimate the system performance. Moreover, numerical results show that with the antenna-diversity technique, the BER performance improves significantly, especially in perfect channel-estimation cases. It is also found that the channel-estimation error limits the benefit of antenna diversity. By increasing the length of the channel estimator and the amplitude of the pilot symbol, more accurate channel estimation can be achieved, so that the BER performance is improved.

The broadband orthogonal frequency and code division multiplexing (OFCDM) system with two-dimensional spreading (time and frequency domain spreading) is becoming a very attractive technique for high-rate data transmission in future wireless communication systems. In this paper, a quasianalytical study is presented on the downlink performance of the OFCDM system with hybrid multi-code interference (MCI) cancellation and minimum mean square error (MMSE) detection. The weights of MMSE are derived and updated stage by stage of MCI cancellation. The effects of channel estimation errors and sub-carrier correlation are also studied. It is shown that the hybrid detection scheme performs much better than pure MMSE when good channel estimation is guaranteed. The power ratio between the pilot channel and all data channels should be set to 0.25, which is a near optimum value for the two-dimensional spreading system with time domain spreading factor (N/sub T/) of 4 and 8. On the other hand, in a slow fading channel, a large value of the channel estimation window size /spl gamma/N/sub T/, where /spl gamma/ is an odd integer, is expected. However, /spl gamma/=3 is large enough for the system with N/sub T/=8 while /spl gamma/=5 is more desirable for the system with N/sub T/=4. Although performance of the hybrid detection degrades in the presence of the sub-carrier correlation, the hybrid detection still works well even the correlation coefficient is as high as 0.7. Finally, given N/sub T/, although performance improves when the frequency domain spreading factor (N/sub F/) increases, the frequency diversity gain is almost saturated for a large value of N/sub F/ (i.e., N/sub F/ /spl ges/ 32).

Performances of optimum and minimum mean square error (MMSE) receivers for variable spreading factor orthogonal frequency and code division multiplexing (VSF-OFCDM) systems are compared in this paper. In VSF-OFCDM systems, the existence of multicode interference (MCI) in the frequency domain due to frequency-selective fading channels dramatically degrades the system performance. A quasi-analytic bit error rate performance is presented in the presence of imperfect channel estimations. Numerical results show that with linear computation complexity the MMSE receiver can improve system performance significantly by suppressing the MCI, although it cannot perform as well as the optimum receiver. Thus, with a small number of code channels, an optimum receiver can be employed to achieve better performance, whereas the MMSE receiver is suitable for a system with a large number of code channels due to simple complexity. In addition, the MMSE receiver is more robust than the optimum receiver to the different configurations of system parameters, e.g., spreading factors. Moreover, it is found that pilot channel power should be carefully assigned by making tradeoffs between the channel estimation quality and the received SNR for each code data channel.

In this letter, the performance of multicarrier code-division multiple-access (CDMA) systems is studied in the presence of narrowband interference for future ultra-wideband (UWB) communications. A Nakagami fading channel is assumed, and notch filters along with diversity techniques are used in the multicarrier CDMA receiver. A complete performance analysis of error probability is given. It is shown that when the number of subcarriers jammed by narrowband interference is small, the multicarrier receiver without notch filters can work well, due to the gain of frequency diversity from nonjammed subcarriers. On the other hand, when the number of subcarriers jammed by the narrowband interference is large, using notch filters can improve the multicarrier system performance significantly.

Adaptive antenna arrays have been widely studied to reduce multiuser interference by spatial suppression. The lattice algorithm, based on least square estimation, is investigated to realize adaptive antenna beamforming. Both analytical and simulation results show that this algorithm not only has fast convergence, but also good multiuser interference suppression performance. Furthermore, the antenna weights iteration processes are discussed in different multiuser interference conditions.

Multicode code division multiple access (CDMA) is a new transmission scheme for flexible and high-speed data communications. The basic idea of multicode CDMA, is to assign multiple channelization codes to any given user. If these channel codes are orthogonal, the self-interference among them can be eliminated in an additive white Gaussian noise (AWGN) channel. However, in a multipath environment, these intrauser signals from different delay paths no longer maintain orthogonality and, thus, cause interference, i.e., multicode interference (MCI), to each other. In high-speed data networks, where the number of users is much less than in the voice networks, the MCI may represent a large portion of the total interference and has a great impact on the system performance. A complex spreading multicode wideband CDMA receiver with RAKE structure and multistage MCI cancellation is studied. By pilot aided channel estimation, the MCI associated with the reference user is regenerated and subtracted from the received signal by a cancellation factor of /spl lambda/. A complete and consolidated theoretical analysis is presented to show that the system performance is significantly improved by the MCI cancellation. The optimal cancellation factor in the kth stage is approximated by /spl lambda//sub opt//sup (k)//spl les/1-2P/sub e//sup (k-1)/, where P/sub e//sup (k-1)/ the error probability of the (k-1)th stage. The optimal value of each stage can be chosen from 0.5 to 0.85 for a wide range of signal to noise ratios.

We introduce the slow frequency hopping (SFH) technique to the multicarrier (MC) code-division multiple-access (CDMA) systems for overlay situations. Using lower chip rate, which results in a narrower spectrum for each carrier and hopping the signal in frequency, the MC direct sequence (DS)/SFH system achieves better performance than the MC DS CDMA system in most cases, especially when the bandwidth of the narrowband interference (NBI) is narrower than one subchannel. It also exhibits a stable performance against the variations of the NBI location and bandwidth. When there is no NBI, the two systems perform approximately the same. The evaluation is performed over a frequency selective Rayleigh fading channel, with both Gaussian approximation and Monte Carlo simulation.

Multicode CDMA is a new transmission scheme for flexible and high-speed data communications. In a multipath environment, these intra-user multicode signals from different delay paths no longer maintain their orthogonality, thus causing significant interference, i.e. multicode interference (MCI), to each other. A multicode CDMA receiver with RAKE structure and multi-stage MCI cancellation is proposed. By pilot aided channel estimation, the MCI associated with the reference user is regenerated and subtracted from the received signal. The detailed receiver structure is presented and theoretical analysis is given to show that the system performance is significantly improved by the MCI cancellation. It is shown that the optimal cancellation factor in the k-th stage can be approximated by /spl lambda//sub opt//sup (k)//spl les/1-2P/sub e//sup (k-1)/, where P/sub e//sup (k-1)/ is the error probability of the (k-1)th stage.

Adaptive power control has widely been used in DS/CDMA systems to overcome the so-called "near-far" problem. This paper studies the adaptive open-loop power control of a cellular CDMA system, which is overlaid in the downlink by a narrowband signal. The effects of downlink power allocation schemes to power control error in the presence of narrowband interference are analyzed. In order to get a minimum power control error in the CDMA overlay situations, an optimum downlink power allocation scheme is used, which works well for a wide range of signal to narrowband interference ratio.

The coherent RAKE reception of wideband code division multiple access (W-CDMA) signals with complex spreading is considered. A general multipath-fading channel model is assumed. A dedicated pilot channel, which is separate from the data channels, is used for the purpose of channel estimation. Based on a digital implementation, the coherent demodulation scheme is presented. Pilot channel estimation error, due to multiple access and multipath interference, is studied. The system performance is evaluated by means of the bit error rate (BER). The analysis shows that the error of channel estimation significantly degrades system performance and can be effectively suppressed by low pass filters (LPFs). A discussion on the envelope variation of complex spread signals is also included, which illustrates that the complex spread signal has a more stable envelope than the dual-channel spread signal. The power ratio of pilot to data channels should be chosen in the range of 0.2 to 0.4 (or -7 to -4 dB), in order to achieve maximum system capacity.

The performance of a coherent delay-locked tracking scheme for direct-sequence/spread-spectrum systems using half-sine or triangular chip waveforms for early and late despreading sequences is analyzed. The effect of band-limiting on the received signals is considered. Mean time to lose lock (MTLL) and root mean square (rms) tracking error of the delay-locked loop (DLL) are compared with that of a conventional DLL which uses rectangular chip despreading waveforms. Linear and nonlinear (based on the renewal process approach) analyses are employed to evaluate the performance of the DLL. Results show that the use of either the half-sine or triangular chip waveform reduces the rms tracking error and increases the MTLL considerably when the early-late spacing is approximately between 0.7-1.3 chip times. The results apply in particular to the commonly used DLL using one chip early-late spacing. Computer simulation for band-limited signals confirms the analytical results. The use of these despreading chip waveforms also reduces tracking offset in multipath environments.

An analytical study of the performance of fast frequency-hopped (FFH), M-ary orthogonal frequency-shift keyed noncoherent modulation with linear combining of square-law envelopes in the presence of multitone interference is presented. The multiple equal-power interference tones are assumed to correspond to some of the possible FFH/M-ary orthogonal signaling tones. It is also assumed that the channel fading characteristics of the signal and interference tones are independent. We evaluate the effect of the channel fading on the system's performance as a function of various parameters, such as the number of hops per symbol, the signal power to multitone interference power ratio, and the number of interference tones. Our numerical results indicate that by use of square-law time diversity combining, a large number of hops per symbol make the bit-error probability of the system more sensitive to the fading of multitone interference. Finally, the analysis has been proven valid by simulation.

This paper presents the behavior of reflection coefficients of a stochastic gradient lattice (SGL) filter applied to a code division multiple-access overlay system. Analytic expressions for coefficients for a two-stage filter are derived in a Rayleigh fading channel with the presence of narrow-band interference and additive white Gaussian noise. It is shown that the coefficients of the lattice filter exhibit separate tracking and convergent properties, and that compared to an LMS filter, the lattice filter provides fast rate of convergence, while having good capability of narrow-hand interference suppression.

The effects of channel fading of multitone interference on the performance of multiple hops per symbol fast frequency-hopped (FFH), M-ary orthogonal frequency-shift keyed (MFSK) noncoherent systems are analytically investigated. The multiple equal-power interference tones are assumed to correspond to some of the possible FFH/M-ary orthogonal signaling tones. It is also assumed that the channel fading characteristics are independent for the signal tone and interference tone. We evaluate how the effect of the multitone interference fading on system performance is changed by various parameters, such as the number of interference tones, the number of hops per symbol and the modulation order. In addition, our numerical results indicate that the larger number of hops per symbol makes the system performance more sensitive to the fading of multitone interference.

This paper extends and complements previous research we have performed on the performance of nonadaptive narrowband suppression filters when used in cellular code-division multiple-access (CDMA) overlay situations. An adaptive least mean square (LMS) filter is applied to a cellular CDMA overlay in order to reject narrowband interference. An accurate expression for the steady-state tap-weight covariance matrix is derived for the real LMS algorithm for arbitrary statistics of the overlaid interference. Numerical results illustrate that when the ratio of the narrowband interference bandwidth to the spread spectrum bandwidth is small, the LMS filter is very effective in rejecting the narrowband interference. Furthermore, it is seen that the performance of the LMS filter in a CDMA overlay environment is not significantly worse than the performance of an ideal Wiener filter, assuming the LMS filter has had sufficient time to converge.

Direct sequence code division multiple access communications is a promising approach to cellular mobile communications, which operates in an environment characterized by multipath Rician fading. In this paper, the CDMA network is assumed to share common spectrum with a narrowband microwave user. Because of the presence of the narrowband waveform, an interference suppression filter at each CDMA receiver is employed to reject the narrowband interference. The problem of interference from adjacent cells is also considered. Average power control is assumed to combat the near/far problem, and multipath diversity, in conjunction with simple interleaved channel coding, is considered for improving the performance of the CDMA system.>

This paper investigates the average bit error rate performance of a direct sequence code division multiple access system operating over a multipath Rician fading channel (which models indoor radio propagation in factories). We consider both phase-shift-keying modulation with coherent demodulation and differential phase-shift-keying modulation with noncoherent demodulation. Predetection multipath diversity (equal gain combining) is considered for improving the BER performance.>

A multiple-hopes-per-symbol fast-frequency-hopped spread-spectrum multiple-access (FFH-SSMA) system for indoor radio communication is treated. The data are Reed-Solomon (RS) encoded and then modulate a carrier by means of M-ary frequency-shift-keying (MFSK) The indoor radio channel is described as a multipath (Rayleigh or Rician) slowly fading channel. The receiver uses predetection diversity by linearly combining the squared enveloped of the different hops of the same MFSK symbol. The bit error rate (BER) performane is evaluated, and how the performance is influenced by various parameters, such as the number of hops per symbol, the size of the symbol alphabet, the ratio of specular power to fading power, and the ratio of nonreference user power to reference user power, is investigated.>

The authors derive close upper and lower bounds on the average bit error probability for hybrid direct-sequence/slow-frequency-hopped spread-spectrum multiple-access (DS/SFH-SSMA) systems with noncoherent DPSK demodulation, using predetection diversity (selection combining and equal gain combining) in conjunction with interleaved channel coding (Hamming

The authors investigate the bit-error-rate (BER) performance of hybrid direct-sequence/slow-frequency-hopped spread-spectrum multiple-access (DS/SFH-SSMA) systems operating over a multipath Rician-fading channel (which models indoor radio propagation in factories). They consider both phase-shift-keying (PSK) modulation with coherent demodulation and differential phase-shift-keying (DPSK) modulation with noncoherent demodulation. Predetection multipath diversity (maximal ratio combining for coherent reception and equal gain combining for noncoherent reception) and simple interleaved channel coding are employed for improving the BER performance. The BER of both coherent and noncoherent hybrid systems is obtained using a Gaussian interference approximation.>