Emerging from recent advances in foundation models, Large Wireless Models (LWMs) represent a new paradigm of general-purpose intelligence for wireless communications that transcends task-specific engineering. The success of foundation models is critically underpinned by scaling laws, which provide a predictable roadmap for how performance scales with resources. However, established scaling laws from language and vision, charting performance as a power-law of model and dataset sizes, are ill-suited for the wireless domain, as their core formulations cannot model the structured nature of the physical channel. To address this, we propose a novel wireless scaling law that extends the classical formulation by modeling two wireless-native factors: channel heterogeneity and discretization granularity. These two factors reshape scaling behavior via nested linear and power-law relationships, recasting the scaling law's parameters (notably the scaling exponent and irreducible loss) from universal constants into dynamic variables dictated by the physical environment. Our physics-aware formulation reveals two key insights: first, that compute-optimal scaling is not dictated by a fixed model-data ratio but is instead a dynamic function of heterogeneity and granularity, and second, that this dependency is particularly sensitive to granularity, allowing significant performance to be unlocked from existing data simply by refining its resolution. Crucially, this establishes a reliable roadmap for designing powerful yet resource-efficient LWMs, translating theoretical insights into actionable engineering principles. Extensive experiments validate our wireless scaling law, showing a 32.31% prediction accuracy improvement over classical laws in diverse wireless scenarios where they fail.

With the growing demands for marine environmental monitoring, collaborative operations, and marine resource development, high-speed and reliable Internet of Things (IoT) communication has become essential for maritime activities. This paper presents a wideband channel measurement campaign at 3.3 GHz in complex nearshore environments to investigate ship-to-ship (S2S) channel characteristics. The study systematically examines time-frequency stationarity, large-scale fading (LSF), dispersion, and small-scale fading (SSF). Results show that the mean stationarity distance reaches 22.48 m, with an average stationarity bandwidth exceeding 39.95 MHz. The statistical close-in (CI) model provides superior accuracy for path loss modeling, while the shadow fading autocorrelation exhibits a periodic oscillatory pattern, captured by a proposed improved model. Time–frequency–angular analysis indicates weak overall dispersion, with stronger dispersion in complex nearshore environments compared to open sea. Furthermore, RMS delay spread, Doppler spread, and angular spread are accurately modeled by Lognormal, Weibull, and Lognormal distributions, respectively. The Rician distribution dominates SSF envelope modeling, with the K-factor following a Normal distribution (mean 14.28–16.03 dB). These findings provide valuable insights for the design and evaluation of maritime IoT communication systems in nearshore environments.

Large artificial intelligence (AI) models for wireless communications have demonstrated remarkable success across a range of wireless downstream tasks. However, their high computational overhead, low training efficiency, and limited privacy protection pose significant challenges for deployment on resource-constrained terminal devices. To address this issue, we propose a novel distributed framework that utilizes a three-layer cooperative paradigm to effectively achieve cooperation among agents, namely Multi-agent cooperative Large AI Models for Wireless Communications: MaLAM4Com. However, two key challenges in MaLAM4Com are how to effectively extract knowledge from shared information and how to alleviate the significant complexity arising from high-dimensional information sharing. To address these bottlenecks, we introduce federated distillation and Lyapunov cooperation to achieve robust knowledge transfer and consistent dynamic evolution, enabling the agents to capture the intrinsic structure of wireless channels. Subsequently, we innovatively utilize low-dimensional embeddings to facilitate information sharing among agents, significantly reducing cooperation complexity by up to 94% while enhancing privacy protection. This breaks traditional cooperative paradigms that rely on wireless channels. Moreover, we further introduce dataset distillation to enhance training efficiency by synthesizing elite data instead of directly utilizing raw datasets. Numerical results demonstrate that MaLAM4Com significantly outperforms existing baselines, with gains exceeding 45% under low sampling ratios. Remarkably, low-dimensional embeddings have also shown significant advantages in downstream tasks, reducing inference complexity by over 96%.

In this paper, we investigate the channel estimation problem in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, explicitly accounting for both spherical-wave propagation characteristics and spatial non-stationary effects. Building on these properties, we propose a novel model-based deep learning framework that delivers high-accuracy channel estimation with low computational complexity by tightly integrating domain knowledge and data-driven learning. Specifically, the proposed framework comprises three key unfolding networks: a sparse channel recovery network, a codebook update network, and an error cancellation network. The first network, referred to as variational Bayesian inference (VBI)-Net, is derived by unfolding the inverse-free VBI (IF-VBI) algorithm. It enables high-precision sparse channel reconstruction without requiring explicit prior assumptions, by learning the underlying precision distribution directly from data. The second network, gradient (Grad)-Net, is developed by unfolding the gradient ascent procedure, where learnable step sizes are introduced to adaptively refine the parameters of the polar-domain grids. Moreover, Grad-Net captures spatial non-stationary characteristics associated with the polar-domain representation by jointly exploiting gradient information and estimated path parameters. The third network, termed projected gradient descent (PGD)-Net, is constructed by unfolding the PGD algorithm. It iteratively refines the channel estimates and effectively suppresses residual estimation errors induced by spherical-wave propagation and spatial non-stationarity. Extensive numerical simulations demonstrate that the proposed framework significantly outperforms existing methods in both estimation accuracy and computational efficiency. Furthermore, the proposed framework achieves a superior accuracy-complexity tradeoff for practical XL-MIMO systems, delivering enhanced performance while maintaining very low computational complexity.

Symbiotic radio (SR) is a promising technology designed to meet the increasing demand for spectrum-efficient communication. However, the small size of backscatter devices (BDs), which are typically equipped with a single antenna, poses challenges in achieving sufficient diversity or spatial multiplexing, thereby hindering the advancement of SR. To address this issue, we introduce fluid antennas (FAs) into SR, enabling devices to dynamically adjust their positions to create a favorable wireless environment and overcome spatial constraints, thereby achieving significant diversity gains. In this paper, we investigate the uplink performance of FA-assisted SR (FA-SR). First, we propose a novel collaborative cancellation channel estimation scheme based on least squares regression (CC-LSR) for scenarios with imperfect channel state information (CSI). We then derive tight lower bound expressions for the channel capacity under both perfect and imperfect CSI cases and formulate the corresponding weighted sum channel capacity (WSCC) optimization problems. The positions of the FAs and the combining vectors are jointly optimized to maximize the lower bound of the WSCC. To solve these problems, we develop joint optimization methods for both perfect and imperfect CSI scenarios using chaotic sequence-based adaptive particle swarm optimization (CSA-PSO). Nevertheless, the high computational complexity of joint optimization poses challenges for practical implementation. To this end, we propose a progressive optimization framework (POF) tailored to both perfect and imperfect CSI scenarios, in which the original problem is divided into three subproblems that are progressively solved to find locally optimal solutions. Numerical results demonstrate that POF significantly reduces computational complexity with minimal performance loss compared to joint optimization methods, particularly under imperfect CSI conditions.

In strong line-of-sight (LoS) scenarios, the lack of scattering paths leads to significant rank deficiency in the channel, thereby limiting the spatial multiplexing capabilities of multiple-input multiple-output (MIMO) systems. To address this inherent deficiency, reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution for enhancing the channel scattering environment in the context of sixth-generation (6G) networks. However, traditional passive RISs often require a large number of elements to counteract multiplicative fading. In contrast, active RISs (ARISs) can greatly reduce the reflecting elements requirement and effectively overcome the multiplicative fading. This paper investigates using multiple ARISs to reshape the wireless channel and formulates the effective rank (ER) maximization problem under two ARIS phase-shift models. We propose a multi-phase particle swarm optimization (MPSO) scheme to optimize the phase shifts of the ARISs under a continuous phase-shift model. However, the continuous phase-shift model entails substantial hardware implementation costs. To solve this, we introduce the multi-phase maximum cross-swapping algorithm (MMCA) under a discrete phase-shift model. Furthermore, we explore the impact of ARIS deployment positions on the channel ER and employ the sparrow search algorithm (SSA) to adjust the ARIS positions. The SSA-MPSO scheme is then proposed, which alternately optimizes the deployment positions and phase shifts of multiple ARISs, achieving an ER close to the theoretical upper bound. Simulation results demonstrate that the proposed scheme significantly outperforms baseline schemes, effectively enhancing the spectral efficiency.

Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, relying on a single central processing unit (CPU) in CF massive MIMO systems is not scalable in practical networks, requiring the introduction of multiple CPUs for more efficient and feasible transmission. In this paper, we investigate a CF massive MIMO system with multiple CPUs. To obtain flexible and cost-efficient deployment, we propose to use wireless x-haul links instead of wired ones. More specifically, we assume that both the fronthaul links from the APs to the corresponding CPU and the backhaul links between CPUs operate under millimeter wave (mmWave) networks. Taking into account a tradeoff between the degree of centralized coordination and the signal overhead on the backhaul links, we consider four levels of multiple CPUs cooperation schemes from fully centralized to fully distributed. In addition, we propose a binary search method to allocate the backhaul capacities for maximizing the sum spectral efficiency (SE). Simulation results show that mmWave backhaul amplifies the compression noise introduced by mmWave fronthaul, leading to a more pronounced impact on the SE of systems. In this case, the centralized processing scheme can generate more compression noise due to the larger data overhead on the backhaul link, making the distributed processing scheme a superior processing scheme, especially when dealing with a large number of APs or significant distances between CPUs.

Cell-free (CF) massive multiple-input multiple-output (MIMO) has recently emerged as a highly promising technology for supporting future six-generation (6G) networks, owing to its unique ability to provide high data rates and reliable connectivity. However, a primary challenge in CF massive MIMO is severe inter-user interference, which is caused by densely located user equipments (UEs) and the presence of imperfect channel state information (CSI). Fortunately, the rate-splitting (RS) strategy offers significant benefits by enabling partially interference decoding, thereby greatly enhancing overall system performance. In this paper, we investigate the performance of uplink RS in CF massive MIMO systems. Considering the inevitable channel estimation errors caused by pilot contamination, we first derive a novel closed-form expression for characterizing spectral efficiency (SE). Moreover, we propose two innovative decoding strategies tailored to the 6G scenario, highlighting their role in enhancing the interference management capabilities of RS, while balancing decoding performance with computational complexity. To ensure successful decoding of each sub-message to the greatest extent possible, we devise an optimization-based power control scheme to maximize the minimum SE of the sub-messages, and propose a low-complexity scheme for comparative analysis. Additionally, we investigate the total energy efficiency (EE) of the system and propose a power control scheme for maximizing EE by exploiting fractional programming (FP) theory. Simulation results corroborate our theoretical expressions and demonstrate that both RS and the proposed power control schemes can significantly improve both SE and EE.

Cell-free (CF) massive multiple-input multiple-output (MIMO) has emerged as a promising technique to deliver uniform signal coverage and high data rates. However, employing low-precision hardware in user equipment introduces susceptibility to hardware impairments (HI), resulting in significantly degraded channel state information (CSI) accuracy. Fortunately, rate-splitting multiple access (RSMA) has been proposed as a robust solution to mitigate the adverse effects of imperfect CSI by performing message splitting at the transmitter and successive interference cancellation (SIC) at the receiver. In this paper, we incorporate RSMA into CF massive MIMO systems to tackle the problem posed by imperfect CSI. Taking into account inevitable pilot contamination, we first derive a novel and closed-form expression for the spectral efficiency (SE) to analytically characterize the performance of RSMA-enabled CF massive MIMO systems under spatially correlated Rician fading channels. Subsequently, we focus on optimizing the decoding order, power allocation, and fronthaul weights to maximize the system’s sum SE. To address this mixed-integer nonlinear programming (MINLP) problem, we initially propose an alternating optimization (AO)-based optimization method that decomposes the original intractable problem into three manageable subproblems, which are iteratively handled until convergence. Considering the significant computational complexity associated with the AO-based approach, we further propose a proximal policy optimization (PPO)-based method to establish an effective and low-complexity optimization framework. Simulation results unveil the detrimental impact of HI on both CSI accuracy and the overall sum SE performance. In particular, the presence of HI introduces residual interference that limits the performance gains achievable through additional RSMA layers, especially in strong line-of-sight scenarios, highlighting the trade-off between these gains and the SIC-related costs in terms of computational complexity and decoding latency.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, conventional CF massive MIMO systems typically employ fixed-position antennas (FPAs) at access points (APs), which limits the exploitation of spatial degrees of freedom (DoFs) for antenna position optimization. To address this issue, we propose the use of fluid antennas (FAs) in place of FPAs, enabling more DoFs at APs and leading to a novel FA-aided CF massive MIMO (FA-CF) architecture. In this paper, we investigate the uplink spectral efficiency (SE) of FA-CF systems with imperfect channel state information (CSI). We design a minimum mean-square error (MMSE)-based channel estimation scheme to estimate the aggregated channels between APs and user equipments (UEs). We further derive achievable SE expressions for both centralized and distributed processing schemes, including fully centralized processing (FCP), large-scale fading decoding (LSFD), and equal-gain decoding processing (EGDP). Moreover, we formulate a mean-square error (MSE) minimization problem based on the signal transmission model. To solve this problem, we develop an efficient algorithm that combines orthogonal matching pursuit (OMP) with binary search to jointly optimize FA positions and the combining matrix. In addition, we propose a protective weak-ordering (PWO) strategy to enhance the SE of the FCP scheme. Numerical results demonstrate that FA-CF significantly outperforms conventional CF systems in terms of SE, even with a limited number of antennas, and maintains strong robustness under imperfect CSI or heavy UE loads by adaptively adjusting antenna positions. These results highlight FA-CF as a promising architecture offering enhanced SE and robustness for future wireless systems, particularly in scenarios where large-scale AP deployment is infeasible or cost-constrained.

The distributed deployment of communication infrastructure is a promising evolutionary trend in the next-generation wireless communication systems, as exemplified by the novel cell-free massive multiple-input multiple-output (CF mMIMO) technology. In user-centric CF mMIMO systems, synchronization among access points (APs) is a critical challenge that significantly impacts the effectiveness of coherent joint processing gains. In this paper, we investigate a CF mMIMO system featuring distributed AP deployments and low-resolution analog-to-digital converters (ADCs). To guarantee precise phase synchronization, we first propose two double-layer AP clustering approaches for rapid synchronization using the Leader-Follower paradigm: one based on the K-means algorithm and the other utilizing classical graph theory with geographical distance metrics in AP deployment. Specifically, in the first layer, a designated Leader AP keeps synchronization with its serving secondary Follower-1 APs, while in the second layer, each Follower-1 AP communicates with its neighboring Follower-2 APs. Next, we propose novel phase synchronization and carrier frequency synchronization strategies among APs based on an over-the-air synchronization signal transmission mechanism, which enables mutual calibration without transmitting any measurements to the central processing unit via fronthaul links. Furthermore, we consider the effect of quantization accuracy of radio frequency hardware on synchronization performance, thereby facilitating the adoption of low-cost components. Finally, simulation results demonstrate that synchronization precision can be significantly improved, reaching values on the order of$10^{-5}$. Additionally, even with moderately coarse ADC quantization, near-optimal performance can be achieved in practical scenarios.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former’s estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.

The integration of reconfigurable intelligent surfaces (RIS) with cell-free massive MIMO (CF mMIMO) represents a compelling paradigm for satisfying the stringent reliability and latency demands of ultra-reliable low-latency communication (URLLC). In this framework, distributed access points (APs) provide substantial macro-diversity gains, while dynamically controllable RIS elements facilitate enhanced signal propagation. This paper investigates a practical RIS-aided CF mMIMO system designed for URLLC applications, where communications occur through RIS-reflected links under realistic spatially correlated Rayleigh fading channels, with practical impairments such as RIS phase estimation errors and electromagnetic interference explicitly considered. To evaluate reliability in the short-packet regime, we adopt the decoding error probability (DEP) as the performance metric and derive its analytical expression based on user-side SINR. We formulate a non-convex optimization problem to minimize the maximum DEP among users by jointly optimizing AP beamforming, RIS phase shifts, and blocklength allocation. A hybrid solution framework is proposed, combining deep reinforcement learning for continuous variables with a differential evolution (DE) algorithm for discrete blocklength optimization. Simulation results demonstrate the superior performance of the proposed method over alternating optimization and genetic algorithm (GA) baselines. Notably, increasing the number of AP antennas and transmission blocklength improves network availability, although gains saturate due to inter-user interference and diminishing returns. Moreover, the proposed DE-based algorithm for blocklength optimization consistently outperforms the GA method in terms of both solution quality and computational efficiency.

Reconfigurable intelligent surfaces (RIS) are emerging as a crucial technology to address the energy consumption challenges posed by the widespread deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF mMIMO) systems within future sixth-generation (6G) networks. However, most existing studies on RIS-aided CF mMIMO systems assume ideal hardware and static channel conditions, which deviate from practical deployment scenarios. This work analyzes the performance of a RIS-aided CF mMIMO system by incorporating the combined effects of hardware impairments from non-ideal transceivers and channel aging caused by user mobility. We first characterize both direct and cascaded channels between APs and user equipment, modeling them using correlated Rician fading to capture realistic propagation effects. The overall channel is then estimated via the minimum mean square error method under perfect and imperfect line-of-sight phase knowledge, and we derive an analytical expression for the instantaneous spectral efficiency (SE). We also derive the closed-form expressions of the use-and-then-forget bound with the maximum-ratio transmission precoding method. Building on these insights, we establish an efficient joint optimization framework for beamforming in the AP and phase-shift adaptations in the RIS, exploring an alternating optimization method and a deep-reinforcement learning (DRL)-based algorithm. The numerical results validate our theoretical analysis, illustrating the impact of hardware impairments and channel aging on SE. Although the DRL-based method is scalable and adapts well to dynamic environments, its high computational and memory demands pose challenges for real-time deployment, highlighting a trade-off between performance and feasibility.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for an energy-efficient RIS-aided CF mMIMO system. To address the associated computational complexity and communication power consumption, we advocate for user-centric dynamic networks in which each user is served by a subset of APs rather than by all of them. Based on the user-centric network, we formulate a joint precoding and AP selection problem to maximize the energy efficiency (EE) of the considered system. To solve this complex nonconvex problem, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme. Moreover, we propose an adaptive power threshold-based AP selection scheme to further enhance the EE of the considered system. To reduce the computational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy logic (FuZ) strategy into the MARL scheme to accelerate convergence. The simulation results show that the proposed FuZ-based MARL cooperative architecture effectively improves EE performance, offering a 85% enhancement over the zero-forcing (ZF) method, and achieves faster convergence speed compared with MARL. It is important to note that increasing the transmission power of the APs or the number of RIS elements can effectively enhance the spectral efficiency (SE) performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the quality of service and EE performance.

In this paper, we investigate the low-complexity distributed combining scheme design for near-field cell-free extremely large-scale multiple-input-multiple-output (CF XL-MIMO) systems. Firstly, we construct the uplink spectral efficiency (SE) performance analysis framework for CF XL-MIMO systems over centralized and distributed processing schemes. Notably, we derive the centralized minimum mean-square error (CMMSE) and local minimum mean-square error (LMMSE) combining schemes over arbitrary channel estimators. Then, focusing on the CMMSE and LMMSE combining schemes, we propose five low-complexity distributed combining schemes based on the matrix approximation methodology or the symmetric successive over relaxation (SSOR) algorithm. More specifically, we propose two matrix approximation methodology-aided combining schemes: Global Statistics & Local Instantaneous information-based MMSE (GSLI-MMSE) and Statistics matrix Inversion-based LMMSE (SI-LMMSE). These two schemes are derived by approximating the global instantaneous information in the CMMSE combining and the local instantaneous information in the LMMSE combining with the global and local statistics information by asymptotic analysis and matrix expectation approximation, respectively. Moreover, by applying the low-complexity SSOR algorithm to iteratively solve the matrix inversion in the LMMSE combining, we derive three distributed SSOR-based LMMSE combining schemes, distinguished from the applied information and initial values.

Movable antenna (MA) technology has recently attracted significant research attention as a promising solution for enhancing wireless network performance. However, conventional MAs can only effectively serve users in close proximity, resulting in restricted coverage. To overcome this limitation, in this paper, we explore a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided MA system. First, we propose a penalty-based block coordinate descent optimization algorithm tailored to the new constraints imposed by BD-RIS-aided MA systems. Specifically, our method decouples the inherently non-convex and coupled antenna distance constraints by introducing auxiliary optimization variables. Subsequently, the resulting problem is efficiently addressed via alternating optimization, with closed-form updates for the auxiliary variables. Furthermore, recognizing the challenges posed by large-scale BD-RIS deployments, which have the potential for serving a substantial number of users, traditional centralized optimization frameworks encounter considerable difficulties, including high computational complexity, excessive communication overheads, as well as limited scalability with increasing system size. To address these limitations, we propose an efficient asynchronous alternating direction method of multipliers (AS-ADMM) scheme aimed at maximizing the sum rate. Our numerical results demonstrate that the BD-RIS-aided MA system achieves superior performance compared to both conventional fixed position antenna and BD-RIS-aided systems. Furthermore, the proposed AS-ADMM framework can achieve a trade-off between performance and computational overhead, highlighting its potential for practical implementation in large-scale wireless communication networks.

Accurate acquisition of channel state information (CSI) is essential for fully harnessing the potential of stacked intelligent metasurfaces (SIMs) in communication systems. In this paper, we address the channel estimation (CE) problem in SIM-based multi-user (MU) millimeter-wave (mmWave) near-field communication systems. To address the severe path loss and blockage in mmWave communication systems, many meta-atoms are typically integrated into each layer of the SIM. Then, the number of radio frequency (RF) chains at the base station (BS) is fewer than that of meta-atoms per layer, resulting in an underdetermined problem. Additionally, the increase in the number of meta-atoms in each layer expands the SIM’s near-field region, leading to the user equipment (UEs) being mostly situated in this region, necessitating precise modeling of the channel under the spherical wavefront assumption. To address these issues, we introduce a compressed sensing (CS)-based CE protocol to tackle the underdetermined problem. In contrast to the traditional CS-based estimation framework, we investigate a polar-domain channel representation to tackle the severe energy spread effect of the classical angular-domain channel representation in near-field communication systems. Specifically, we design a novel polar-domain transform matrix for uniform planar arrays (UPAs), thereby transforming the CE problem into a sparse recovery task of the paths’ support set and complex gains. To overcome the limitations of the sparse Bayesian learning (SBL) framework in tackling high-dimensional dictionaries, we propose a low-complexity polar-domain SBL (LCPD-SBL) algorithm, which significantly reduces computational complexity without compromising estimation accuracy. Numerical simulation results demonstrate that the proposed polar-domain transform matrix yields a better estimation accuracy than traditional angular-domain approaches. Additionally, the proposed LCPD-SBL algorithm can be faster than existing SBL methods by up to 4× while sustaining the same estimation performance.

As a key technology of future communication systems, reconfigurable intelligent surfaces (RISs) can intelligently control wireless signal reflections, thereby optimizing propagation channels and enhancing communication performance. However, the indoor channel characteristics and communication performance of RISs in real-world deployment environments remain insufficiently studied. In this paper, we develop a RIS-based measurement platform for simultaneous channel measurement and link-level performance analysis, followed by verification and validation in a laboratory environment. Utilizing this platform, we conduct wideband channel measurement and performance analysis work in indoor scenarios at 2.6 GHz band. Multiple RIS phase shift codebooks are employed, including the 1-bit discrete Fourier transform (DFT) codebook, the Ring-type codebook, and the conditional sample mean (CSM) codebook. Based on these measurements, two empirical path loss (PL) models, namely the floating-intercept (FI) model and the close-in (CI) model, are fitted to the measured data. Furthermore, we comprehensively analyze and compare the channel characteristics of RIS-assisted communications, including shadow fading (SF), multipath components (MPCs) statistics, the Rician K-factor (KF), and root mean square (RMS) delay spread. Finally, we evaluate and compare the wireless coverage and link-level performance of various RIS reflection schemes. The findings on propagation characteristics and communication performance provide essential insights that can support the future application and deployment of RIS-assisted communication systems.

In strong line-of-sight (LoS) communication systems, the lack of scattering paths leads to significant rank deficiency in the channel matrix, thereby limiting the spatial multiplexing gain of multiple-input multiple-output (MIMO) systems. To address this inherent deficiency, this paper utilizes multiple active reconfigurable intelligent surfaces (ARISs) to reshape the wireless channel and solve the effective rank (ER) maximization problem. We propose a multi-phase particle swarm optimization (MPSO) scheme to optimize the phase shifts of the ARISs. Furthermore, we explore the impact of ARIS deployment positions on the channel ER and employ the sparrow search algorithm (SSA) to adjust the ARIS positions. Then we propose the SSA-MPSO scheme, which alternately optimizes the deployment positions and phase shifts of multiple ARISs, achieving an ER close to the theoretical upper bound. Simulation results demonstrate that the proposed scheme significantly outperforms baseline schemes, effectively enhancing the spatial multiplexing gain under imperfect channel state information (CSI).

This paper investigates the covert communication (CC) performance of a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) and rate splitting (RS) systems operating over Rician fading channels. Alice applies RS and NOMA to the downlink transmission of two legitimate users aided by the STAR-RIS in the presence of two non-colluding illegal users. Specifically, closed-form expressions for detection error probability, optimal detection threshold, minimum detection error probability (MDEP) of the warden, and the covert rate of the NOMA user pair are derived. The accuracy of the derived results is verified through Monte Carlo simulations. The results demonstrate that the MDEP depends only on the power allocation factor of the covert users and is independent of the transmit power or STAR-RIS deployment distance. Furthermore, the RS-NOMA system exhibits superior CC performance compared to the conventional NOMA system.

Reconfigurable intelligent surface (RIS) holds significant potential to enhance wireless communications by offering good performance, high security, and great efficiency. In particular, RIS provides notable benefits in improving wireless location accuracy for user equipments (UEs), which are often overlooked. In this paper, we utilize RIS for received signal strength (RSS)-based indoor fingerprinting localization. To improve positioning accuracy, we propose a two-stage RIS configuration selection approach. Specifically, in the first stage, we select the RIS configuration that contributes significantly to positioning. In the second stage, a supervised learning approach is used to select features, effectively reducing the large state space of the RIS. The effectiveness of the proposed RIS-assisted RSS fingerprinting localization technique is validated through simulation and field test results.

Reconfigurable intelligent surface (RIS) has emerged as a promising key technology for sixth-generation (6G) wireless communication systems, primarily due to its capability to intelligently manipulate the propagation environment. In this paper, we conduct comprehensive measurements of the wireless channel and link-level performance of an RIS-assisted communication system in indoor corridor scenarios. We analyze the time dispersion characteristics and channel capacity under both RIS intelligent reflection (RIS-IR) and without RIS (WR) cases for comparative evaluation. The results demonstrate that wideband channel characteristics have a notable impact on the performance of RIS-assisted communications. Specifically, we model the fading distribution of the visual-line-of-sight (VLoS) path introduced by the RIS and establish a corresponding tapped-delay-line (TDL) channel model. The insights derived from this study offer theoretical foundations and practical guidelines for the deployment and performance evaluation of RIS-assisted communication systems.

Cell-free massive multiple-input multiple-output symbiotic radio (CF-SR) has recently been introduced as a promising solution for the Internet of Things (IoT), offering cost advantages and more uniform coverage performance for user devices. However, most previous studies assume perfect hardware, which is impractical in IoT systems. In this article, we investigate the uplink performance of CF-SR systems in the presence of hardware impairments (HWIs). We adopt two novel schemes for hybrid combining, namely hybrid maximum ratio (HMR) and hybrid local minimum mean square error (HL-MMSE), both of which effectively enhance the spectral efficiency (SE) of the backscattering link. We derive closed-form expressions for the achievable SE of both conventional MR and HMR combining schemes, taking into account the imperfect channel state information (CSI) and HWIs. The simulation results that both the direct and backscattering links are primarily limited by HWIs including multiplicative and additive distortions from the device side. We apply the differential evolution (DE) algorithm for power control to maximize the minimum SE, and the results show that the DE algorithm improves the minimum SE by approximately 52%, avoiding further degradation of the SE of the weakest device due to the impact of HWIs.

Reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) technology has tremendous potential to revolutionize wireless communications by dynamically adapting wireless channels to boost average rate and energy efficiency (EE) of Internet of Things (IoT) networks for meeting the specifications of ultra-reliable and low-latency communications (URLLC). In this paper, we study the downlink harvested energy (HE), uplink rate, and total EE of the wireless-powered RIS-aided CF-mMIMO communication system with hardware impairments under finite blocklength. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Specifically, based on the unique characteristics of the channel fading model and the RIS deployment location, we propose a novel RIS phase shift design according to the line-of-sight (LoS) components of channels. Furthermore, we derive the average HE and uplink rate in closed form with a two-layer decoding method. We also validate the effectiveness of the proposed RIS phase shift design and the derived closed-form expressions by Monte Carlo simulations. Moreover, it is interesting to find that local minimum mean squared error (L-MMSE) combining is recommended to meet the requirements of URLLC, including communication reliability and delay. More notably, the numerical results show that the RIS-aided system with impaired hardware exhibits even superior performance, compared to the system with ideal hardware and more APs but lacking the assistance of RISs.

This paper explores the potential of employing rate-splitting multiple access to enhance the achievable rate and energy efficiency (EE) of an uplink cell-free massive multiple-input multiple-output (MIMO) system, where the access points (APs) are configured with low-resolution analog-to-digital converters (ADCs) to minimize the hardware expense and power consumption. Taking the large-scale fading decoding, ADC quantization, and imperfect successive interference cancellation into consideration, a rigorous closed-form rate expression is derived within Ricean fading environments. This analytical framework facilitates an in-depth analysis of the rate performance with respect to various system parameters. To quantify the benefits of low-resolution ADCs, a power consumption model is subsequently incorporated into the analysis, facilitating an evaluation of the system’s EE. Furthermore, the optimization of power control coefficients and receiver weights is tackled through the formulation of weighted sum-rate (WSR) and EE maximization problems. Two efficient alternative algorithms are then proposed to determine their optimal solutions. The theoretical propositions and the efficacy of the proposed WSR and EE optimization algorithms are substantiated through comprehensive simulations.

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of the fifth generation (5G) has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, the sixth generation (6G) is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an artificial intelligence (AI)-enabled cross-domain channel modeling and orthogonal time–frequency space–time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications (SACs), AI-assisted Internet of Things (IoT), semantic communications (SCs), and digital twin (DT) networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed. © 2026 IEEE

Cell-free symbiotic radio (CF-SR) is a promising technology to meet the demands of good quality-of-service and spectrum-efficient communications. However, the introduction of SR brings additional interference terms, which can seriously degrade the performance of the CF-SR systems. To suppress the interference, we adopt a rate-splitting (RS) transmission scheme to CF-SR. In this paper, we derive downlink spectral efficiency (SE) expressions of the CF-SR system with RS. Furthermore, in a conventional two-phase (TP) channel estimation scheme, the direct link causes heavy interference to the backscatter link, consequently diminishing the accuracy of the backscatter-link channel estimation. To this end, we propose a collaborative cancellation (CC) channel estimation scheme, which can eliminate the interference from the direct link and thus improve the accuracy of the backscatter-link channel estimation. Moreover, we derive the novel closed-form SE expressions under the CC channel estimation scheme using maximum ratio (MR) precoding. Simulation results show that the normalized mean square error (NMSE) of the CC channel estimation is consistently better than the one of the TP channel estimation, both on the direct and backscatter links. Furthermore, the advantages of the CC channel estimation scheme on the backscatter link can be further amplified in scenarios with a sufficient number of pilots. In addition, simulation results demonstrate that both the CC channel estimation scheme and the RS transmission scheme can provide significant improvements.

In this paper, we investigate the downlink distributed transmit beamforming problem in partially connected cell-free massive multiple-input multiple-output (CF mMIMO) systems, specifically designed to satisfy the stringent requirements of ultra-reliable and low-latency communication (URLLC) services. First, we propose a scalable framework that incorporates partial access points (APs) to serve active user equipment (UE), with a reduced energy consumption and computational complexity. To this end, a min-max optimization problem is formulated for minimizing the decoding error probability (DEP) among URLLC services. Then, a graph neural network (GNN)-based strategy called G4PCF is proposed for partially connected CF mMIMO, which takes into account the underlying characteristics of the problem. Furthermore, by leveraging the temporal correlation in channel state information acquired from the previous frame, we develop a parallel G4PCF (P-G4PCF) scheme that significantly reduces both the signaling overhead and computation delay for minimizing DEP of the worst UE. Simulation results demonstrate that the proposed G4PCF and P-G4PCF architectures exhibit excellent scalability for CF mMIMO networks, offering superior performance over existing methods in terms of quality of service outage probability. Notably, P-G4PCF excels in supporting URLLC services with short frame durations and highly correlated channels, while G4PCF performs better under lower channel correlation. Moreover, the proposed algorithms can significantly enhance the application of GNNs into CF mMIMO systems with a reduced complexity compared with the classical weighted minimum mean-squared error algorithm, especially with delay sensitive services.

Reconfigurable intelligent surface (RIS) has the potential to revolutionize wireless communications by dynamically controlling wireless channels to boost spectral efficiency (SE) and energy efficiency (EE), towards meeting the advanced specifications of Internet of Things (IoT) networks. In this context, we study the downlink harvested energy (HE), uplink SE and total EE of the RIS-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system with wireless power transfer (WPT) technology. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Based on the unique characteristics of the channel fading model and the RIS deployment location, we put forward a novel RIS phase shift design scheme according to the line-of-sight (LoS) components of channels and verify its effectiveness. Furthermore, we derive the average HE and uplink SE in closed form with a two-layer decoding method (i.e., the maximal ratio combining (MRC) at APs is called first-layer decoding and the large-scale fading decoding (LSFD) at CPU is called second-layer decoding.) under the assumptions of both perfect and imperfect statistical channel state information (CSI). The results verify the derived closed-form expressions by Monte-Carlo simulations. Increasing the number of RIS elements further improves the uplink SE and total EE with the two-layer decoding. Since the statistical CSI is unknown in practical scenarios, we propose an acquisition method for the statistical CSI applicable to this system. Simulation results validate the efficiency of the proposed statistical CSI acquisition method. Furthermore, it is interesting to find that better statistical CSI estimation can be achieved with more coherent blocks of pilot.

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.

The increasing connectivity and automation in the Internet of vehicles (IoV) have significantly heightened the risk of network attacks, making intrusion detection systems (IDS) a crucial component of security measures in intelligent transportation systems (ITS). To address this challenge, we propose an advanced intrusion detection method integrating knowledge graph (KG) and artificial intelligence (AI) techniques, termed IDS-IKGAI, to enhance the security of IoV infrastructures. In our proposed scheme, we first preprocess an intrusion detection dataset specific to IoV, i.e., feature selection and extraction, that can be represented as triples using the resource description framework (RDF). These RDF triples are used to construct a knowledge graph, capturing the semantic relationships among the features. Next, we map the knowledge graph to a vector space, to build a labeled dataset for machine learning. To train and predict potential intrusions, the random forest (RF) and light gradient boosting machine learning (LightGBM) algorithms are investigated. Experimental evaluations demonstrate the effectiveness of our proposed scheme, with around F1 scores of 99.99% for RF and 99.93% for LightGBM, outperforming conventional benchmark models.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at different locations. One approach to cope with this is to employ true-time-delay lines (TTDs)-based beamforming to control the range and trajectory of near-field beam squint, known as the near-field controllable beam squint (CBS) effect. In this paper, we investigate the user localization in near-field wideband XL-MIMO systems under the beam squint effect and spatial non-stationary properties. Firstly, we derive the expressions for Cramér-Rao Bounds (CRBs) for characterizing the performance of estimating both angle and distance. This analysis aims to assess the potential of leveraging CBS for precise user localization. Secondly, a user localization scheme combining CBS and beam training is proposed. Specifically, we organize multiple subcarriers into groups, directing beams from different groups to distinct angles or distances through the CBS to obtain the estimates of users’ angles and distances. Furthermore, we design a user localization scheme based on a convolutional neural network model, namely ConvNeXt. This scheme utilizes the inputs and outputs of the CBS-based scheme to generate high-precision estimates of angle and distance. The numerical results derived from CRBs reveal that the inherent spatial non-stationary characteristics notably increase the CRB for angle, but have an insignificant impact on the CRB for distance estimation. In addition, the CRBs for both angle and distance decrease with increasing bandwidth and number of subcarriers. More importantly, our proposed ConvNeXt-based user localization scheme achieves centimeter-level accuracy in localization estimates.

Cell-free massive multiple-input multiple-output symbiotic radio (CF-mMIMO-SR) is a promising technology to address the requirements of high-rate and spectrum-efficient communication for the Internet of Things (IoT). However, in the conventional CF-mMIMO-SR system aided by backscatter devices (BDs), the backscatter link is impacted by double fading without any supplementary compensation, resulting in significantly low spectral efficiency (SE) on the backscatter link. To address this issue, we propose the usage of reconfigurable intelligent surfaces (RISs) instead of BD for symbol-level reflection on the backscatter link, leading to a novel RIS-aided CF-mMIMO-SR (RIS-CF-SR) system. In this paper, we conduct a comprehensive analysis of the RIS-CF-SR system considering different levels of cooperation among the access points (APs). Specifically, we analyze the uplink SEs of four different implementations with arbitrary linear processing on both the direct and backscatter links. Moreover, we investigate different signal cancellation schemes based on full or local channel state information (CSI) to improve the SE of the backscatter link. Through the simulation results, we find that RISs can significantly improve the SE of the backscatter link due to the large number of reflection elements, whereas additional appropriate signal processing schemes are required for the direct link. More specifically, from Level 1 to Level 3, RIS-CF-SR does not have significant advantage in SE over BD-CF-SR on the direct link. At Level 4, RIS-CF-SR can outperform BD-CF-SR on the direct link with the MMSE combining scheme.

To enhance the capacity and reliability of next-generation wireless communication systems, the novel cell-free massive multiple-input multiple-output (mMIMO) has emerged as a pivotal technology in satisfying the stringent quality of service requirements of massive network-connected devices. In this paper, we propose a partially coherent joint transmission (PCJT) approach that draws insights from both coherent and non-coherent joint transmission (NCJT) strategies. Specifically, we design the downlink transmit beamformers to maximize the weighted sum rate (WSR) and compare the performance in three distinct joint transmission modes, ranging from coherent and partially coherent, to non-coherent joint transmission. Specifically, a non-convex optimization problem is formulated that incorporates multiple data stream transmission and transmit power constraints. Given the intractability of the problem, the weighted minimum mean square error (WMMSE) approach is introduced to transform it into an equivalent form, which facilitates the development of a low-complexity and low-interaction reduced WMMSE (R-WMMSE) beamforming algorithm design to acquire an effective solution. For further reducing communication overhead and improving convergence rates, we propose a novel graph convolution network-based unfolding technique for R-WMMSE algorithm. It significantly reduces the number of iterations required while achieving similar performance to the original WMMSE algorithm, thus alleviating the signaling overhead burdens in distributive implementation. Simulation results demonstrate the significant performance gains achieved by the proposed algorithm in terms of superior WSR and rapid convergence performance. Furthermore, it is evident that the performance of PCJT can promote the performance achieved by NCJT, positioning it as an alternative between the existing two joint transmission strategies.

To enhance global data transmission, uncrewed aerial vehicle (UAV)-aided space-air-ground integrated networks (SAGIN) represent a pivotal direction for future advancements. In this paper, we focus on the trajectory optimization problem with the goal of maximizing the energy efficiency (EE), thereby balancing the system capacity with energy expenditure. To this end, we first introduce a cell-free SAGIN network where UAVs function as flying access points to serve ground user equipment (GUE). Given that the transmission power of satellite direct-to-cell devices typically exceeds that of GUEs, we investigate the interference effect and derive exact closed-form expressions for the uplink spectral efficiency. In order to improve the service access efficiency, a GUE grouping scheme based on density distribution is proposed. Then, an effective EE analysis model is established considering the power consumption of fixed-wing UAVs. To solve the UAV trajectory optimization problem, two algorithms over two timescales are proposed: a successive convex approximation strategy and a multi-agent reinforcement learning (MARL)-based algorithm. In particular, to reduce the algorithmic complexity, we employ a shared Critic network in the proposed MARL algorithm to reduce the training parameters. Importantly, our approach comprehensively optimizes the UAV trajectory, acceleration, and velocity parameters. The results show that the proposed GUE grouping algorithm and the MARL-based optimization algorithm demonstrate adaptability in dynamic time-varying environments.

Cell-free massive multiple-input multiple-output (mMIMO) offers significant advantages in mobility scenarios, mainly due to the elimination of cell boundaries and strong macro diversity. In this paper, we examine the downlink performance of cell-free mMIMO systems equipped with mobile-APs utilizing the concept of unmanned aerial vehicles, where mobility and power control are jointly considered to effectively enhance coverage and suppress interference. However, the high computational complexity, poor collaboration, limited scalability, and uneven reward distribution of conventional optimization schemes lead to serious performance degradation and instability. These factors complicate the provision of consistent and high-quality service across all user equipments in downlink cell-free mMIMO systems. Consequently, we propose a novel scalable framework enhanced by multi-agent reinforcement learning (MARL) to tackle these challenges. The established framework incorporates a graph neural network (GNN)-aided communication mechanism to facilitate effective collaboration among agents, a permutation architecture to improve scalability, and a directional decoupling architecture to accurately distinguish contributions. In the numerical results, we present comparisons of different optimization schemes and network architectures, which reveal that the proposed scheme can effectively enhance system performance compared to conventional schemes due to the adoption of advanced technologies. In particular, appropriately compressing the observation space of agents is beneficial for achieving a better balance between performance and convergence.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However, further capacity enhancement requires the deployment of more access points (APs), which will lead to high costs and high energy consumption. To address this issue, in this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into CF mMIMO systems to enhance AP capabilities. The key point is that SIM performs precoding-related matrix operations in the wave domain. As a consequence, each AP antenna only needs to transmit data streams for a single user equipment (UE), eliminating the need for complex baseband digital precoding. Then, we formulate the problem of joint AP-UE association and precoding at APs and SIMs to maximize the system sum rate. Due to the non-convexity and high complexity of the formulated problem, we propose a two-stage signal processing framework to solve it. In particular, in the first stage, we propose an AP antenna greedy association (AGA) algorithm to minimize UE interference. In the second stage, we introduce an alternating optimization (AO)-based algorithm that separates the joint power and wave-based precoding optimization problem into two distinct sub-problems: the complex quadratic transform method is used for AP antenna power control, and the projection gradient ascent (PGA) algorithm is employed to find suboptimal solutions for the SIM wave-based precoding. Finally, the numerical results validate the effectiveness of the proposed framework and assess the performance enhancement achieved by the algorithm in comparison to various benchmark schemes. The results show that, with the same number of SIM meta-atoms, the proposed algorithm improves the sum rate by approximately 275% compared to the benchmark scheme.

In this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into cell-free (CF) massive multiple-input multiple-output (mMIMO) systems to enhance access point (AP) capabilities and address high power consumption and cost challenges. Specifically, we investigate the uplink performance of a SIM-enhanced CF mMIMO system and propose a novel system framework. First, the closed-form expressions of the spectral efficiency (SE) are obtained using the unique two-layer signal processing framework of CF mMIMO systems. Second, to mitigate inter-user interference, an interference-based greedy algorithm for pilot allocation is introduced. Third, a wave-based beamforming algorithm for SIM is proposed, based only on statistical channel state information, which effectively reduces the fronthaul costs. Finally, two different power control algorithms are proposed to improve the performance of UE with inferior channel conditions. The results indicate that increasing the number of SIM layers and meta-atoms leads to significant performance improvements and allows for a reduction in the number of APs and AP antennas, thus lowering the costs. In particular, the best SE performance is achieved with the deployment of 20 APs plus 1200 SIM meta-atoms. Finally, the proposed wave-based beamforming algorithm can enhance the SE performance of SIM-enhanced CF-mMIMO systems by 57%, significantly outperforming traditional CF mMIMO systems.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for substantial enhancement in both the spectral efficiency and spatial resolution. However, the achievable performance limits of various promising XL-MIMO configurations have yet to be fully evaluated, compared, and discussed. In this paper, we develop an effective degrees of freedom (EDoF) performance analysis framework specifically tailored for near-field XL-MIMO systems. We explore five representative distinct XL-MIMO hardware designs, including uniform planar array (UPA)-based with infinitely thin dipoles, two-dimensional (2D) continuous aperture (CAP) plane-based, UPA-based with patch antennas, uniform linear array (ULA)-based, and one-dimensional (1D) CAP line segment-based XL-MIMO systems. Our analysis encompasses two near-field channel models: the scalar and dyadic Green’s function-based channel models. More importantly, when applying the scalar Green’s function-based channel, we derive EDoF expressions in the closed-form, characterizing the impacts of the physical size of the transceiver, the transmitting distance, and the carrier frequency. In our numerical results, we evaluate and compare the EDoF performance across all examined XL-MIMO designs, confirming the accuracy of our proposed closed-form expressions. Furthermore, we observe that with an increasing number of antennas, the EDoF performance for both UPA-based and ULA-based systems approaches that of 2D CAP plane and 1D CAP line segment-based systems, respectively. Moreover, we unveil that the EDoF performance for near-field XL-MIMO systems is predominantly determined by the array aperture size rather than the sheer number of antennas.

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port communication theory, which considers impedance matching among the source, transmission medium, and load to facilitate efficient power transfer. Specifically, we first investigate the impact of insertion loss, mutual coupling, and other factors on the performance of multi-port matching networks. Next, to further improve system performance, we explore two important deep unfolding designs for the multi-port matching networks: beamforming and power control, respectively. For the hybrid beamforming, we develop a deep unfolding framework, i.e., projected gradient descent (PGD)-Net based on unfolding projected gradient descent. For the power control, we design a deep unfolding network, graph neural network (GNN) aided alternating optimization (AO)-Net, which considers the interaction between different ports in optimizing power allocation. Numerical results verify the necessity of considering insertion loss in the dynamic metasurface antenna (DMA) performance analysis. Besides, the proposed PGD-Net based hybrid beamforming approaches approximate the conventional model-based algorithm with very low complexity. Moreover, our proposed power control scheme has a fast run time compared to the traditional weighted minimum mean squared error (WMMSE) method.

The use of predictor antennas (PAs) has significant potential to enhance wireless channel prediction performance in high-speed railway (HSR) communications. The PA system features two sets of antennas installed on the roof of a vehicle. The PA is located at the front of the vehicle and is used to predict the channel observed by the receive antenna (RA), which is located behind the PA. The PAs can be integrated with dense pilots spatially, but the prediction performance decreases when channel estimations are sparse. Therefore, this paper first proposes a multiple PAs (mPAs) system combined with interpolation for sparse channel estimations. Subsequently, recognizing the need for PAs to measure all antenna channels in the estimation interval, we propose an informer-based mPAs system. This system predicts future RA channels in parallel, effectively solving the problem of error propagation in sequential prediction methods. Simulation results demonstrate that as the prediction horizon extends, the proposed informer-based mPAs system outperforms others. Finally, we investigate how varying velocities impact prediction accuracy. It was found that the prediction accuracy of a single PA system performs well at low speeds but drops rapidly at high speeds. Moreover, our proposed informer-based mPAs system achieves higher prediction horizons and maintains efficiency at high speeds.

The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the operational principles of artificial neural networks (ANN). Leveraging its structure for direct EM signal processing alongside its low-power consumption, SIM holds promise for enhancing system performance within wireless communication systems. In this paper, we focus on SIM-assisted multi-user multi-input and single-output (MU-MISO) system downlink scenarios in the transmitter. We proposed a joint optimization method for SIM phase shift configuration and antenna power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm to efficiently improve the sum rate. The results show that the proposed algorithm outperforms both deep deterministic policy gradient (DDPG) and alternating optimization (AO) algorithms. Furthermore, increasing the number of meta-atoms per layer of the SIM is always beneficial. However, continuously increasing the number of layers of SIM does not lead to sustained performance improvement.

Semantic communication (SemCom) holds promise for reducing network resource consumption while achieving the communications goal. However, the computational overheads in jointly training semantic encoders and decoders—and the subsequent deployment in network devices—are overlooked. Recent advances in Generative artificial intelligence (GAI) offer a potential solution. The robust learning abilities of GAI models indicate that semantic decoders can reconstruct source messages using a limited amount of semantic information, e.g., prompts, without joint training with the semantic encoder. A notable challenge, however, is the instability introduced by GAI’s diverse generation ability. This instability, evident in outputs like text-generated images, limits the direct application of GAI in scenarios demanding accurate message recovery, such as face image transmission. To solve the above problems, this paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. Moreover, in response to security concerns, we introduce the application of covert communications aided by a friendly jammer. The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.

Cell-free (CF) massive multiple-input multiple-output (MIMO) system has emerged as a highly promising technology, primarily due to its ability to improve coverage and performance. However, one of the key challenges is their reliance on perfect channel state information (CSI). To address this issue, we propose the incorporation of a rate-splitting (RS) strategy, which has been proven to effectively mitigate the negative impact of imperfect CSI. In this paper, we investigate CF massive MIMO systems that utilize the RS strategy. We derive a closed-form expression for the RS-assisted CF massive MIMO system in the uplink, while accounting for pilot contamination. We also present four decoding schemes that can be implemented in practical systems. Our extensive simulations reveal that CF massive MIMO systems utilizing the RS strategy outperform those that do not in terms of sum spectral efficiency (SE). These findings emphasize the effectiveness of RS technology in mitigating the negative effects of imperfect CSI in CF massive MIMO systems. The insights gained from this research can serve as a basis for the design and optimization of future CF massive MIMO systems, ultimately improving their performance and expanding their applicability in diverse scenarios.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) system in the presence of electromagnetic interference (EMI) and channel aging with a Rician fading channel model between the base station (BS) and user equipment (UE). Specifically, we derive the closed-form expression for downlink spectral efficiency (SE) with maximum ratio transmission (MRT) precoding. The Monte-Carlo simulation supports the theoretical results, demonstrating that amplifying the weight of the line-of-sight (LoS) component in Rician fading channels can boost SE, while EMI has a detrimental impact. Furthermore, continuously increasing the number of RIS elements is not an optimal choice when EMI exists. Nonetheless, RIS can be deployed to compensate for SE degradation caused by channel aging effects. Finally, enlarging the RIS elements size can significantly improve system performance.

Reconfigurable intelligent surfaces (RISs) and cell-free (CF) massive multiple-input multiple-output (MIMO) are two promising technologies for sixth-generation (6G) networks. This paper investigates the achievable uplink sum rate of a RIS-aided CF massive MIMO system considering electromagnetic interference (EMI) at the RISs and imperfect channel state information (CSI). Our focus is on proposing an integrated approach that optimizes the beamforming at the access points (APs) and the RIS phase shift alternately using successive convex approximation and penalty convex-concave procedures to maximize the uplink sum rate. The results demonstrate that the proposed algorithm significantly improves the performance of the RIS-aided CF massive MIMO system and effectively mitigates the interference caused by EMI and imperfect CSI. Additionally, we find that the negative impact of EMI becomes more pronounced as the channel uncertainty increases. Moreover, increasing the number of RIS reflecting elements proves beneficial, but the returns diminish as the number of RIS elements becomes sufficiently large. Furthermore, deploying RIS beyond a certain limit of EMI power leads to degradation in system performance.

Reconfigurable intelligent surface (RIS) is a cost-effective component to enhance wireless energy harvest and spectral efficiency (SE) of wireless communications systems. In the paper, we investigate the performance of a RISs-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system over Rician fading channels in conjunction with wireless power transfer (WPT). User equipments (UEs) harvest energy from the energy signal transmitted from access points (APs) with the help of RISs during the downlink and use it for the uplink pilot and data transmission. Specifically, we derive a closed-form expression for the uplink SE when using a two-layer decoding method and verify the accuracy of the derived results by Monte Carlo simulation. The results show that increasing the number of APs or the number of RIS elements is always beneficial for the system SE improvement. Furthermore, increasing the number of RIS elements can lead to a reduction in the required number of APs without a decrease in the average SE, which means that significant hardware and energy costs can be greatly reduced.

This article considers the uplink of a cell-free massive multiple-input–multiple-output (MIMO) symbiotic radio (CF-mMIMO-SR) system. Conventional combining schemes cannot be used directly to detect the signal of the direct link due to its heavy suppression for the backscatter link. To this end, we propose two hybrid combining schemes, including the hybrid maximum ratio (MR) and hybrid local minimum mean square error (L-MMSE) combining schemes, which take into account the superposition of the two local channel estimation vectors at the access points (APs). When the number of APs goes to infinite, the asymptotic spectral efficiency (SE) of CF-mMIMO-SR with different combining schemes is analyzed. We prove that the conventional combining schemes tend to cause the effective signal of the backscatter link to disappear while the hybrid combining schemes can obtain good performance on the backscatter link. Meanwhile, the performance gap between the two on the direct link is small. In addition, we derive closed-form expressions with the conventional MR combining and hybrid MR combining schemes over independent Rayleigh fading channels. Moreover, we derive the achievable uplink SE expressions with an effective signal-to-interference-and-noise ratio (SINR) for a finite number of antennas. Simulation results verify our theoretical analysis and demonstrate that hybrid combining schemes perform much better on the backscatter link than conventional combining schemes. Specifically, compared to the hybrid MR scheme, the hybrid L-MMSE scheme offers a huge improvement in 95% likely SE, and has negligible performance loss on the direct link.

This letter investigates secrecy performance for cognitive transmissions, where a secondary user (SU) shares same spectrum with a primary user (PU) simultaneously ensuring the Quality of Service (QoS) of primary transmissions. Additionally, an eavesdropper (Eve) overhears cognitive transmissions from SU to base station (BS). To against eavesdropping attacks, a novel artificial noise-aided rate splitting (ANRS) scheme is proposed, where PU emits artificial noise to confuse Eve and SU adopts rate splitting (RS). The numerical results of secrecy outage probability indicates that the ANRS scheme achieves better secrecy performance than that of AN without RS (ANWRS) and of RS without AN (RSWAN) schemes.

In this paper we study the effective degree of freedom (EDoF) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. We consider two XL-MIMO hardware designs, uniform planar array (UPA) based and continuous aperture (CAP) based XL-MIMO, as well as two representative near-field channel models: scalar Green function based and dyadic Green function with triple polarization based models. First, for UPA-based XL-MIMO with a discrete array aperture, we evaluate the EDoF performance by applying discrete channel matrices generated by the scalar or dyadic Green channel model. Then, for CAP-based XLMIMO, a tailored EDoF performance evaluation framework for a two-dimensional (2D) CAP plane based system is constructed by leveraging asymptotic analysis and extending the analysis approaches for a one-dimensional (1D) CAP line segment based system. This framework incorporates the triplepolarized auto-correlation kernel function, which can efficiently capture the impact of multiple polarization on the EDoF performance. Numerical results show that, with an increase in the number of antennas, the UPA-based XL-MIMO system can achieve an EDoF performance close to the EDoF performance for the CAP plane based XL-MIMO system. Moreover, the EDoF performance can be enhanced by the multiple polarization in channels and increased physical size of the transceiver.

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency (EE), ultralow latency, and ultrahigh reliability. Cell-free (CF) massive multiple-input-multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this article, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments (HIs) and electromagnetic interference (EMI). We summarize the corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as millimeter wave (mmWave) and terahertz (THz), simultaneous wireless information and power transfer (SWIPT), next-generation multiple access (NGMA), and unmanned aerial vehicle (UAV). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.

This paper investigates the uplink energy efficiency (EE) of a wireless-powered cell-free massive multiple-input multiple-output (mMIMO) system with hardware impairments, considering Rician fading and maximum ratio processing, anchored in linear minimum mean-squared error (LMMSE) channel estimation. The transceivers of access points (APs) and user equipments (UEs) are non-ideal with hardware impairments. The closed-form expressions of the total uplink (UL) EE and the average harvested energy (HE) are derived by employing a non-linear energy harvesting model and coherent transmission schemes. An optimization problem is formulated to maximize the total uplink EE, in which power control coefficients for APs and UEs, and the large-scale fading decoding vectors are considered. An alternating algorithm based on successive convex approximation (SCA) is proposed to tackle the complexity issue of the non-convex optimization problem. The numerical results show that the proposed algorithm can significantly enhance the total uplink EE comparing with the equal power allocation strategies. Moreover, it is revealed that the hardware impairments on the UEs can be the primary limitation to the total uplink EE in comparison with those on the APs, however, the impact can be effectively mitigated by the proposed algorithm.

In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a promising advancement for enabling future mobile networks. To address the computational complexity and communication power consumption associated with conventional centralized optimization, we focus on user-centric dynamic networks in which each user is served by an adaptive subset of access points (AP) rather than all of them. We begin our research by analyzing a joint resource allocation problem for energy-efficient CF XL-MIMO systems, encompassing cooperative clustering and power control design, where all clusters are adaptively adjustable. Then, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme, which offers an effective strategy to tackle the challenges of high-dimensional signal processing. In the section of numerical results, we compare various algorithms with different network architectures. These comparisons reveal that the proposed MARL-based cooperative architecture can effectively strike a balance between system performance and communication overhead, thereby improving energy efficiency performance. It is important to note that increasing the number of user equipments participating in information sharing can effectively enhance SE performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the number of participants and EE performance.

The reconfigurable intelligent surface (RIS) technology holds great promise for the advancement of future sixth-generation networks. However, existing research on RIS-assisted communication systems often relies on ideal hardware and static channel conditions, which are impractical in real-world scenarios. In this study, we assess the performance of a RIS-assisted communication system, considering the combined effects of hardware impairments caused by imperfect transceivers and channel aging resulting from user mobility. To achieve this, we analyze the direct and cascade channels between the base station and the user, assuming correlated Rician distributions. We employ the linear minimum mean square estimation method to estimate the overall channel and derive a closed-form expression for the uplink spectral efficiency (SE). By formulating an optimization problem for RIS phase shift, we maximize SE using the projected gradient ascent algorithm. Monte Carlo simulations reveal the impact of channel aging and hardware impairments on system performance. While practical RIS implementations may introduce phase estimation error in the reflected signal, these errors can be mitigated through phase shift optimization. Overall, our results highlight the significant potential of RIS technology in addressing challenges posed by imperfect hardware and users’ mobility.

Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant attention has been generated by its considerable advantages in augmenting degrees of freedom. In this paper, we first investigate a CF XL-MIMO system with base stations equipped with XL-MIMO panels under a dynamic environment. Then, we propose an innovative multi-agent reinforcement learning (MARL)-based power control algorithm that incorporates predictive management and distributed optimization architecture, which provides a dynamic strategy for addressing high-dimension signal processing problems. Specifically, we compare various MARL-based algorithms, which shows that the proposed MARL-based algorithm effectively strikes a balance between spectral efficiency (SE) performance and convergence time. Moreover, we consider a double-layer power control architecture based on the large-scale fading coefficients between antennas to suppress interference within dynamic systems. Compared to the single-layer architecture, the results obtained unveil that the proposed double-layer architecture has a nearly 24% SE performance improvement, especially with massive antennas and smaller antenna spacing.

Cell-free massive multiple-input multiple-output (mMIMO) is a promising technology to empower next-generation mobile communication networks. In this paper, to address the computational complexity associated with conventional fingerprint positioning, we consider a novel cooperative positioning architecture that involves certain relevant access points (APs) to establish positioning similarity coefficients. Then, we propose an innovative joint positioning and correction framework employing multi-agent reinforcement learning (MARL) to tackle the challenges of high-dimensional sophisticated signal processing, which mainly leverages on the received signal strength information for preliminary positioning, supplemented by the angle of arrival information to refine the initial position estimation. Moreover, to mitigate the bias effects originating from remote APs, we design a cooperative weighted K-nearest neighbor (Co-WKNN)-based estimation scheme to select APs with a high correlation to participate in user positioning. In the numerical results, we present comparisons of various user positioning schemes, which reveal that the proposed MARL-based positioning scheme with Co-WKNN can effectively improve positioning performance. It is important to note that the cooperative positioning architecture is a critical element in striking a balance between positioning performance and computational complexity.

With the growing popularity of wearable devices, body-centric networks have become the focus of Internet of Things (IoT) research. However, malicious eavesdroppers and attackers pose threats to user privacy and network secrecy. Fortunately, physical layer security (PLS) communication and covert communication (CC) have been introduced and can be used in body-centric networks. However, network performance of both secrecy techniques has not been analyzed with an accurate wireless fading model. To fill this research gap, we study a body-centric IoT communication system with information secrecy features based on PLS and CC techniques. By adopting the Alternate Rician Shadowed fading model, we derive closed-form expressions for the secrecy outage probability and secrecy rate in PLS, and the detection error probability and covert rate in CC. To provide more explicit guidance, an algorithm for the selection of PLS and CC techniques is proposed, which aims to reach a high transmission performance state while ensuring communication security and covertness. The secrecy rate and covert rate are analytically and empirically compared. We insightfully find that, with the help of a friendly jammer, CC performs better than PLS, especially with high transmit power. However, an upper boundary of transmit power exists to ensure communication covertness in CC.

Cell-free massive multiple-input multiple-output (CF mMIMO) is known for its ability to provide ubiquitous connectivity. In this paper, we investigate the achievable spectral efficiency (SE) of a user-centric (UC) CF mMIMO system with both multi-antenna APs and users over joint-correlated Rayleigh fading channels. First, we provide a performance analytical framework for the system with the linear decorrelator and study the impact of hardware impairments (HIs) at transceivers on the uplink SE. Based on that, we discuss the local minimum mean-squared error (MMSE) and partial MMSE combining schemes and the partial large-scale fading decoding (LSFD) method from a scalable point of view. Besides, the exact closed-form SE expression is derived with maximum ratio combining (MRC). Then, we study the MMSE-based successive interference cancelation (MMSE-SIC) detector and give an approximate closed-form SE expression with MRC. In the simulations, we compare the linear decorrelator to the MMSE-SIC detector under different hardware-impaired scenarios. Numerical results correspond to the theoretical analyses and show that the impact of HIs can be mitigated by adding the number of receive antennas.

In Cell-Free Massive multi-input multi-output (CF mMIMO), it is challenging to assign regular pilots due to the pre-log pilot overhead on spectral efficiency (SE). This paper explores a superimposed-pilot-(SP)-assisted CF mMIMO system which avoids the separate pilot training duration by superimposing pilot symbols onto data symbols. We consider spatial-correlated Rician fading channels with and without random phase shifts, where linear minimum-mean-square-error (LMMSE) estimators are performed at each access point locally. Then, we derive the closed-form SE expressions with maximal-ratio (MR) combining. To fill the gap, we introduce novel expressions of MMSE combining vectors and compare their SE performance with approximate MMSE combining vectors. Next, a generic model is provided for the optimal large-scale fading decoding (LSFD), and we derive the closed-form suboptimal LSFD solutions with MR combining. A line-of-sight-based combining scheme is proposed based on the approximate analysis, where closed-form SE expressions are derived using MR and MMSE combining and corresponding optimal LSFD coefficients. Numerical results show that the combination of MMSE and optimal LSFD yields almost 200% enhancement over the combination of MR and simple centralized decoding in 95% likely per-user SE without phase shifts, and 111% enhancement when phase shifts exist.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

To leverage massive distributed data and computation resources in the Internet-of-Things (IoT) networks, federated learning (FL) is considered to be a promising technique with benefits of improved data privacy and communication efficiency. Meanwhile, cell-free massive multiple-input–multiple-output (cell-free massive MIMO) is a promising technology to enable the IoT networks to support FL. By deploying the access points (APs) closer to the IoT devices, path loss attenuation can be reduced. However, the performance of FL is still constrained by the limited power resources of IoT devices. To address this issue, we design an energy-efficient FL scheme over cell-free IoT networks by formulating an optimization problem to minimize the total energy consumption of the IoT devices participating in the FL process. To solve the intractable problem in hand, by exploiting its unique structure, we decompose it into three subproblems that facilitate the development of the proposed scheme. First, we derive the optimal central processing unit (CPU) operating frequency for IoT devices. Then, we design an optimal power allocation scheme to mitigate the straggler effect. Next, a nonlinear programming method is adopted to obtain a suboptimal solution for the reformulated problem. Finally, a three-stage algorithm is proposed for energy consumption minimization by considering these subproblems. Simulation results demonstrate the close-to-optimal performance of the proposed algorithm for energy savings compared with three baseline algorithms and the capability to support large numbers of IoT device access by mitigating the straggler effect.

Cell-free (CF) massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for realizing future beyond-fifth generation (B5G) networks. In this paper, we consider a practical spatially correlated RIS-aided CF massive MIMO system with multi-antenna access points (APs) over spatially correlated fading channels. Different from previous work, the electromagnetic interference (EMI) at RIS is considered to further characterize the system performance of the actual environment. Then, we derive the closed-form expression for the system spectral efficiency (SE) with the maximum ratio (MR) combining at the APs and the large-scale fading decoding (LSFD) at the central processing unit (CPU). Moreover, to counteract the near-far effect and EMI, we propose practical fractional power control (FPC) and max-min power control algorithms to further improve the system performance. We unveil the impact of EMI, channel correlations, and different signal processing methods on the uplink SE of user equipments (UEs). The accuracy of our derived analytical results is verified by extensive Monte-Carlo simulations. Our results show that the EMI can substantially degrade the SE, especially for those UEs with unsatisfactory channel conditions. Besides, increasing the number of RIS elements is always beneficial in terms of the SE, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the existence of spatial correlations among RIS elements can deteriorate the system performance when RIS is impaired by EMI.

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

Cell-free massive multiple-input multiple-output (mMIMO) has recently been proposed to improve cell edge performance. However, most prior works consider perfect hardware impairments (HIs), which are difficult to be achieved in practical systems. This paper studies the impact of HI in an uplink cell-free mMIMO system with both multi-antenna access points (APs) and multi-antenna user terminals (UTs) under the Weichselberger channel model.Firstly, we study a two-layer decoding scheme with local minimum mean-squared error or maximum ratio combining at the AP side and with optimal large-scale fading decoding in the central processing unit. We derive novel closed-form SE expressions and prove that the effect of HI can be mitigated in the case of UTs with multiple antennas. However, the achievable SE is constrained by the pilot contamination and pilot overhead. To this end, the superimposed pilot (SP) transmission method is considered in this paper, where all the coherence intervals are used for both pilot and data symbols transmission. Finally, numerical results verify our derived expressions and reveal the relationship between HI and the number of antennas per UT for different pilot schemes. Note that the advantages of SP over regular pilots disappear when the hardware quality decreases with multi-antenna UTs.

In this paper, we investigate a cell-free massive multiple-input multiple-output system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) for the fully centralized processing scheme and large-scale fading decoding (LSFD) scheme. To further improve the SE performance, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on Iteratively Weighted sum-Minimum Mean Square Error (I-WMMSE) algorithms, which rely on the iterative minimization of weighted Mean Square Error (MSE), are proposed for two processing schemes investigated. Furthermore, with maximum ratio combining applied in the LSFD scheme, we derive novel closed-form achievable SE expressions and optimal precoding schemes. Numerical results validate the proposed results and show that the I-WMMSE precoding schemes can achieve excellent sum SE performance with a large number of UE antennas.

Ultra-Reliable and Low-Latency Communication (URLLC) represents a key ingredient of current 5G and future 6G mobile communication networks. The demanding reliability requirement set for URLLC claims for novel techniques that can deliver the necessary level of reliability without sacrificing the overall system capacity. In this context, this work presents and analyses a hybrid transmission scheme for improved reliability in millimetre wave bands with adaptive diversity combining. The proposed scheme is based on a hybrid approach that combines two links, one in the FR1 band (characterised by lower capacity but higher reliability due to more favourable propagation) and one in the FR2 band (offering higher capacity but experiencing a less reliable connectivity). The proposed scheme dynamically adapts the usage of both links in order to exploit the complementary characteristics of both bands (reliability of FR1 bands and capacity of FR2 bands) by switching between FR2-only and joint FR1-FR2 transmission according to the instantaneous channel quality in the main FR2 link. The performance is evaluated under two popular and well-known diversity combining techniques, namely Selection Combining (SC) and Maximal Ratio Combining (MRC). The obtained results demonstrate that the proposed scheme can achieve the same level of reliability as a continuous dual-link transmission scheme but with a much lower level of links usage and without sacrificing (and indeed enhancing) the capacity, thus making it a suitable candidate to deliver URLLC services in a resource-efficient manner.

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2–4 times higher thanks to the reduced processing and signaling.

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central processing unit in practice. To guarantee data privacy, effective privacy-preserving mechanisms are defined in this paper. In particular, we demonstrate and characterize the possibility in exploiting the inherent quantization error, caused by low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), for privacy-preserving in a FL CF mMIMO system. Furthermore, to reduce the required uplink training time in such a system, a stochastic non-convex design problem that jointly optimizing the transmit power and the data rate is formulated. To address the problem at hand, we propose a novel power control method by utilizing the successive convex approximation approach to obtain a suboptimal solution. Besides, an asynchronous protocol is established for mitigating the straggler effect to facilitate FL. Numerical results show that compared with the conventional full power transmission, adopting the proposed power control method can effectively reduce the uplink training time under various practical system settings. Also, our results unveil that our proposed asynchronous approach can reduce the waiting time at the central processing unit for receiving all user information, as there are no stragglers that requires a long time to report their local updates.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF mMIMO) system with both multi-antenna user equipments (UEs) and access points (APs) over spatially correlated Rayleigh fading channels. In practi-cal CF mMIMO systems, the in-phase and quadrature-phase imbalance (IQI) and low-resolution analog-to-digital converters (ADCs) at the APs are critical for the system performance. Taking these factors into account, the achievable uplink spectral efficiency (SE) is analyzed based on a two-layer decoding scheme. In particular, the maximum ratio (MR) and local minimum mean-square error (L-MMSE) combining are adopted at the APs while the large-scale fading decoding (LSFD) is implemented at the central processing unit (CPU). Furthermore, we derive novel closed-form SE expressions with the MR combining and investigate the SE performance for different combining schemes, quantization bits, and IQI parameters. Numerical results reveal the performance degradations caused by both the low-resolution ADCs and IQI. Additionally, increasing the number of APs is an effective means to promote the system performance.

We consider a practical spatially correlated recon-figurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) system with multi-antenna access points (APs) over spatially correlated Rician fading channels. The minimum mean square error (MMSE) channel estimator is adopted to estimate the aggregated RIS channels. Then, we investigate the uplink spectral efficiency (SE) with the maximum ratio (MR) and the local minimum mean squared error (L-MMSE) combining at the APs and obtain the closed-form expression for characterizing the performance of the former. The accuracy of our derived analytical results has been verified by extensive Monte-Carlo simulations. Our results show that increasing the number of RIS elements is always beneficial, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the effect of the number of AP antennas on system performance is more pronounced under a small number of RIS elements, while the spatial correlation of RIS elements imposes a more severe negative impact on the system performance than that of the AP antennas.

How to manage the interference introduced by the enormous wireless devices is a crucial issue to address in the prospective sixth-generation (6G) communications. The treating interference as noise (TIN) optimality conditions are commonly used for interference management and thus attract significant interest in existing wireless systems. Cell-free massive multiple-input multiple-output (CF mMIMO) is a promising technology in 6G that exhibits high system throughput and excellent interference management by exploiting a large number of access points (APs) to serve the users collaboratively. In this paper, we take the first step on studying TIN in CF mMIMO systems from a stochastic geometry perspective by investigating the probability that the TIN conditions hold with spatially distributed network nodes. We propose a novel analytical framework for TIN in a CF mMIMO system with both Binomial Point Process (BPP) and Poisson Point Process (PPP) approximations. We derive the probability that the TIN conditions hold in close form using the PPP approximation. Numerical results validate our derived expressions and illustrate the impact of various system parameters on the probability that the TIN conditions hold.

In this paper, we investigate a cell-free massive MIMO system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) based on a two-layer decoding structure with maximum ratio (MR) or local minimum mean-square error (MMSE) combining applied in the first layer and optimal large-scale fading decoding method implemented in the second layer, respectively. To maximize the weighted sum SE, an uplink precoding structure based on an Iteratively Weighted sum-MMSE (I-WMMSE) algorithm using only channel statistics is proposed. Furthermore, with MR combining applied in the first layer, we derive novel achievable SE expressions and optimal precoding structures in closed-form. Numerical results validate our proposed results and show that the I-WMMSE precoding can achieve excellent sum SE performance.

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems are expected to implement advanced cooperative communication techniques to let geographically distributed access points jointly serve user equipments. Building on the Team Theory, we design the uplink team minimum mean-squared error (TMMSE) combining under limited data and flexible channel state information (CSI) sharing. Taking into account the effect of both channel estimation errors and pilot contamination, a minimum MSE problem is formulated to derive unidirectional TMMSE, centralized TMMSE and statistical TMMSE combining functions, where CF massive MIMO systems operate in unidirectional CSI, centralized CSI and statistical CSI sharing schemes, respectively. We then derive the uplink spectral efficiency (SE) of the considered system. The results show that, compared to centralized TMMSE, the unidirectional TMMSE only needs nearly half the cost of CSI sharing burden with neglectable SE performance loss. Moreover, the performance gap between unidirectional and centralized TMMSE combining schemes can be effectively reduced by increasing the number of APs and antennas per AP.

High-speed train (HST) communications with orthogonal frequency division multiplexing (OFDM) techniques have received significant attention in recent years. Besides, cell-free (CF) massive multiple-input multiple-output (MIMO) is considered a promising technology to achieve the ultimate performance limit. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both matched filter and large-scale fading decoding (LSFD) receivers in HST communications. HST communications with small cell and cellular massive MIMO-OFDM systems are also analyzed for comparison. Considering the bad effect of Doppler frequency offset (DFO) on system performance, exact closed-form expressions for uplink spectral efficiency (SE) of all systems are derived. According to the simulation results, we find that the CF massive MIMO-OFDM system with LSFD achieves both larger SE and lower SE drop percentages than other systems. In addition, increasing the number of access points (APs) and antennas per AP can effectively compensate for the performance loss from the DFO. Moreover, there is an optimal vertical distance between APs and HST to achieve the maximum SE.

This article investigates the performance of nonorthogonal multiple access (NOMA)-based cell-free massive multiple-input–multiple-output (mMIMO) for the Internet of Things (IoT) considering spatially correlated Rician fading channels. The exact closed form of downlink spectral efficiency (SE) and energy efficiency expressions is derived with three estimators and the maximum ratio transmission by taking the impacts of imperfect successive interference cancellation and pilot contamination into account. Subsequently, the performance of a local-MMSE precoder with the three aforementioned estimators is analyzed. Then, a large-scale fading-based user pairing scheme is proposed to further analyze the system SE. Besides, we formulate the optimum power control design as a max–min problem and a computational efficient suboptimal algorithm is proposed based on the successive convex approximation. Furthermore, our results reveal that the magnitude of the spatial correlation negligibly effects the SE in spatially correlated Rician fading channels. Then, numerical results confirm the positive effect of the proposed power control scheme. Also, our results further illustrate that NOMA-based cell-free mMIMO for IoT provides significant performance gain compared with its counterpart deploying conventional orthogonal multiple-access schemes.

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems show great potentials in low-mobility scenarios, due to cell boundary disappearance and strong macro diversity. However, the great Doppler frequency offset (DFO) leads to serious inter-carrier interference in orthogonal frequency division multiplexing (OFDM) technology, which makes it difficult to provide high-quality transmissions for both high-speed train (HST) operation control systems and passengers. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both fully centralized and local minimum mean square error (MMSE) combining in HST communications. Considering the local maximum ratio (MR) combining, the large-scale fading decoding (LSFD) cooperation and the practical effect of DFO on system performance, exact closed-form expressions for uplink spectral efficiency (SE) expressions are derived. We observe that cooperative MMSE combining achieves better SE performance than uncooperative MR combining. In addition, HST communications with small cell and cellular massive MIMO-OFDM systems are compared in terms of SE. Numerical results reveal that the CF massive MIMO-OFDM system achieves a larger and more uniform SE than the other systems. Finally, the train antenna centric (TA-centric) CF massive MIMO-OFDM system is designed for practical implementation in HST communications, and three power control schemes are adopted to optimize the propagation of TAs for reducing the impact of the DFO.

TeraHertz (THz) communications can satisfy the high data rate demand with massive bandwidth. However, severe path attenuation and hardware imperfection greatly alleviate its performance. Therefore, we utilize the reconfigurable intelligent surface (RIS) technology and investigate the RIS-aided THz communications. We first prove that the small-scale amplitude fading of THz signals can be accurately modeled by the fluctuating two-ray distribution based on two THz signal measurement experiments conducted in a variety of different scenarios. To optimize the phase-shifts at the RIS elements, we propose a novel swarm intelligence-based method that does not require full channel estimation. We then derive exact statistical characterizations of end-to-end signal-to-noise plus distortion ratio (SNDR) and signal-to-noise ratio (SNR). Moreover, we present asymptotic analysis to obtain more insights when the SNDR or the number of RIS’s elements is high. Finally, we derive analytical expressions for the outage probability and ergodic capacity. The tight upper bounds of ergodic capacity for both ideal and non-ideal radio frequency chains are obtained. It is interesting to find that increasing the number of RIS’s elements can significantly improve the THz communications system performance. For example, the ergodic capacity can increase up to 25% when the number of elements increases from 40 to 80, which incurs only insignificant costs to the system.

In this paper, we investigate the performance of an RIS-aided wireless communication system subject to outdated channel state information that may operate in both the near- and far-field regions. In particular, we take two RIS deployment strategies into consideration: (i) the centralized deployment, where all the reflecting elements are installed on a single RIS and (ii) the distributed deployment, where the same number of reflecting elements are placed on multiple RISs. For both deployment strategies, we derive accurate closed-form approximations for the ergodic capacity, and we introduce tight upper and lower bounds for the ergodic capacity to obtain useful design insights. From this analysis, we unveil that an increase of the transmit power, the Rician-$K$factor, the accuracy of the channel state information and the number of reflecting elements help improve the system performance. Moreover, we prove that the centralized RIS-aided deployment may achieve a higher ergodic capacity as compared with the distributed RIS-aided deployment when the RIS is located near the base station or near the user. In different setups, on the other hand, we prove that the distributed deployment outperforms the centralized deployment. Finally, the analytical results are verified by using Monte Carlo simulations.

In this paper, the performance of a mixed Terahertz/free-space optical (THz/FSO) wireless transmission system is studied, where the joint effects of channel fading and pointing errors are considered for both THz and FSO links. Employing the semi-blind amplify-and-forward protocol, we derive the cumulative distribution function and probability density function of the end-to-end signal-to-noise ratio. By applying the derived statistics, exact expressions for the outage probability (OP), average bit error rate (BER), and average channel capacity are obtained. In order to attain useful physical insights, asymptotic OP and average BER expressions are also presented. Based on them, the diversity gain is determined, which is shown to depend on channel fading and pointing errors of both links. In addition, by taking into account the hardware impairments, the OP of the non-ideal hardware system is derived. Moreover, the analysis is extended to multi-antenna scenarios and the asymptotic OP is obtained. Finally, illustrative numerical results are plotted and it can be observed that the path loss, channel fading, pointing errors and hardware impairments lead to a considerable degradation in system performance.

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed. Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known, and thus has low training complexity. More specifically, a deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time and with low computational complexity. It is interesting to note that the spectral efficiency of CF mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization and fits well for both max-sum-rate and max-product optimizations.

Caching popular contents at a large number of access points and edge-clouds is a promising solution to alleviate the increasing backhaul congestion in beyond fifth-generation (B5G) networks. By integrating with cell-free massive multiple-input multiple-output (CF mMIMO), wireless caching can harness their combined virtues, i.e., almost uniform service quality, strong macro-diversity, and reduction of the data traffic from the core network. In this paper, we consider an offline cache-aided scenario with two caching strategies to minimize the total energy consumption (TEC), which are evaluated from the cache hit probability (CHP). The TEC minimization is showed to be NP-complete and, hence, dealt with a proposed greedy algorithm. An adaptive power control policy is proposed to reduce the TEC. We compare CF mMIMO with small cells in terms of the successful content delivery probability (SCDP) and TEC, respectively. The numerical results show that CF mMIMO can offer a much more uniform service, significantly higher SCDP, and lower average TEC when compared to than SC.

Recently, unmanned aerial vehicle (UAV) communications have drawn significant research interests. Meanwhile, cell-free (CF) massive multiple-input multiple-output (MIMO) is proposed as a promising technology to achieve the ultimate performance limits. In this paper, we investigate the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. Take hardware impairments of UAV into account, novel closed-form downlink HE and uplink spectral efficiency (SE) expressions are derived. UAV communications with small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. Our results reveal that CF massive MIMO achieves two and four times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. To this end, SE is a concave function of the time-splitting fraction, and the optimal time-splitting fraction for maximizing SE is determined by the altitude and hardware impairment factor of the UAV.

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE.

Cell-free (CF) massive multiple-input multiple-output (MIMO) is a promising solution to provide uniform good performance for unmanned aerial vehicle (UAV) communications. In this paper, we propose the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. We derive novel closed-form downlink HE and uplink spectral efficiency (SE) expressions that take hardware impairments of UAV into account. UAV communications with current small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. It is significant to show that CF massive MIMO achieves two and five times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. Besides, the large-scale fading decoding receiver cooperation can reduce the interference of the terrestrial user. Moreover, the maximum SE can be achieved by changing the time-splitting fraction. We prove that the optimal time-splitting fraction for maximum SE is determined by the number of antennas, altitude and hardware quality factor of UAVs. Furthermore, we propose three UAV trajectory design schemes to improve the SE. It is interesting that the angle search scheme performs best than both AP search and line path schemes. Finally, simulation results are presented to validate the accuracy of our expressions.

In this study, we consider a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with a mixed analog-to-digital converter (mixed-ADC) and hybrid beamforming architecture, in which antenna selection is applied to achieve intelligent assignment of high- and low-resolution ADCs. Both exact and approximate closed-form expressions for the uplink achievable rate are derived in the case of maximum-ratio combining reception. The impacts on the achievable rate of user transmit power, number of radio frequency chains at a base station, ratio of high-resolution ADCs, number of propagation paths, and number of quantization bits are analyzed. It is shown that the user transmit power can be scaled down inversely proportional to the number of antennas at the base station. We propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, the energy efficiency is investigated, and an optimal tradeoff between the achievable rate and power consumption is discussed. Our results will provide a useful reference for the study of mixed-ADC multi-cell mmWave massive MIMO systems with antenna selection.

Reconfigurable Intelligent Surface (RIS) can create favorable multipath to establish strong links that are useful in millimeter wave (mmWave) communications. While previous works assumed Rayleigh or Rician fading, we use the fluctuating two-ray (FTR) distribution to model the small-scale fading in mmWave frequency. First, we obtain the statistical characterizations of the product of independent FTR random variables (RVs) and the sum of product of FTR RVs. For the RIS-aided and amplify-and-forward (AF) relay systems, we derive exact end-to-end signal-to-noise ratio (SNR) expressions. To maximize the end-to-end SNR, we propose a novel and simple way to obtain the optimal phase shifts at the RIS elements. The optimal power allocation scheme for the AF relay system is also proposed. Furthermore, we evaluate important performance metrics including the outage probability and the average bit-error probability. To validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted to provide interesting insights. It is found that the RIS-aided system can attain the same performance as the AF relay system with low transmit power. More interestingly, as the channel conditions improve, the RIS-aided system can outperform the AF relay system using a smaller number of reflecting elements.

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion.

Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently. We study the physical layer security of the downlink RIS-aided transmission framework for randomly located users in the presence of a multi-antenna eavesdropper. To show the advantages of RIS-aided networks, we consider two practical scenarios: Communication with and without RIS. In both cases, we apply the stochastic geometry theory to derive exact probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-interference-plus-noise ratio. Furthermore, the obtained PDF and CDF are exploited to evaluate important security performance of wireless communication including the secrecy outage probability, the probability of nonzero secrecy capacity, and the average secrecy rate. Monte-Carlo simulations are subsequently conducted to validate the accuracy of our analytical results. Compared with traditional MIMO systems, the RIS-aided system offers better performance in terms of physical layer security. In particular, the security performance is improved significantly by increasing the number of reflecting elements equipped in a RIS. However, adopting RIS equipped with a small number of reflecting elements cannot improve the system performance when the path loss of NLoS is small.

Reconfigurable intelligent surfaces (RISs) have the capability of reconfiguring the wireless environment in a favorable way to improve the quality-of-service (QoS) of wireless communications. This makes RISs a promising candidate to enhance vehicle-to-everything (V2X) applications. This paper investigates the spectrum sharing problem in RIS-aided vehicular networks, in which multiple vehicle-to-vehicle (V2V) links reuse the spectrum already occupied by vehicle-to-infrastructure (V2I) links. To overcome the difficulty of acquiring instantaneous channel state information (CSI) due to the fast varying nature of some V2X channels, we rely upon large-scale (slowly varying) CSI in order to fulfill the QoS requirements of V2I and V2V communications. Particularly, we aim to maximize the sum capacity of V2I links that are used for high-rate content delivery and to guarantee the reliability of V2V links that are used for the exchange of safety information. The transmit power of vehicles, the multi-user detection (MUD) matrix, the spectrum reuse of V2V links, and the RIS reflection coefficients are jointly optimized, which results in a mixed-integer and non-convex optimization problem. To tackle this problem, the outage probability of each V2V link is first approximated by introducing an analytical expression to simplify the formulated problem. By leveraging the block coordinate descent (BCD) method, the considered optimization problem is decomposed into three sub-problems, whose optimal solutions are provided independently and updated alternately to obtain a near-optimal solution. Simulation results verify the theoretical analysis and the effectiveness of the proposed algorithm, as well as unveil the benefits of introducing RISs for enhancing the QoS performance of vehicular communications.

In this paper, we investigate the impact of channel aging on the performance of cell-free (CF) massive multiple-input multiple-output (MIMO) systems with both spatial correlation and pilot contamination. We derive novel closed-form uplink and downlink spectral efficiency (SE) expressions that take imperfect channel estimation into account. More specifically, we consider large-scale fading decoding and matched-filter receiver cooperation in the uplink. The uplink performance of a small-cell (SC) system is derived for comparison. The CF massive MIMO system achieves higher 95%-likely uplink SE than the SC system. In the downlink, the coherent transmission has four times higher 95%-likely per-user SE than the non-coherent transmission. Statistical channel cooperation power control (SCCPC) is used to mitigate the inter-user interference. SCCPC performs better than full power transmission, but the benefits are gradually weakened as the channel aging becomes stronger. Furthermore, strong spatial correlation reduces the SE but degrades the effect of channel aging. Increasing the number of antennas can improve the SE while decreasing the energy efficiency. Finally, we use the maximum normalized Doppler shift to design the SE-improved length of the resource block. Simulation results are presented to validate the accuracy of our expressions and prove that the CF massive MIMO system is more robust to channel aging than the SC system.

In this paper, we investigate the impact of channel aging on the performance of cell-free (CF) massive multiple-input multiple-output (MIMO) systems with pilot contamination. To take into account the channel aging effect due to user mobility, we first compute a channel estimate. We use it to derive novel closed-form expressions for the uplink spectral efficiency (SE) of CF massive MIMO systems with large-scale fading decoding and matched-filter receiver cooperation. The performance of a small-cell system is derived for comparison. It is found that CF massive MIMO systems achieve higher 95%-likely uplink SE in both low-and high-mobility conditions, and CF massive MIMO is more robust to channel aging. Fractional power control (FPC) is considered to compensate to limit the inter-user interference. The results show that, compared with full power transmission, the benefits of FPC are gradually weakened as the channel aging grows stronger.

This paper considers non-orthogonal multiple access (NOMA) based cell-free massive multiple-input multiple-output (mMIMO) systems over spatially correlated Rician fading channels. Closed-form downlink achievable sum-rate expression is derived by taking into account spatial correlation among multi-antenna access points, inter-cluster interference, intra-cluster pilot contamination, and imperfect successive interference cancellation (SIC). In particular, we propose an intra-cluster power allocation design for improving the system performance. Furthermore, we investigate the downlink performance with both minimum mean-squared error (MMSE) and element-wise MMSE channel estimation. It is interesting to find out that the correlation magnitude has a negligible effect on the system sum-rate in spatially correlated Rician fading channels. The numerical results validate the correctness of the presented results and confirm the effectiveness of the proposed power allocation.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology for future wireless networks. One main challenge of realizing practical cell-free massive MIMO is the high power consumption and huge hardware cost for employing high-resolution analog-to-digital converters (ADCs). To tackle this issue, a promising solution is to use low-resolution ADCs. In this paper, we investigate the cell-free massive MIMO system with low-resolution ADCs over spatially correlated channels. We generalize three levels of receiver cooperation and derive a tight closed-form expression of the spectral efficiency (SE) for a centralized receiver cooperation with large-scale fading decoding as a function of the resolution of ADCs. We also investigate the impact of spatial correlation magnitude on the sum SE. Moreover, we proposed a low-complexity power control method for maximizing the sum SE. Numerical results show that the centralized receiver cooperation needs more quantization bits to achieve the ideal performance and the proposed power control is efficient for improving the system performance.

To reduce the hardware cost and power consumption in millimeter wave (mmWave) multiuser massive multiple-input multiple-output (MIMO) systems, a quantized hybrid transmit model with low-resolution digital-to-analog converters (DACs) is introduced. A correlation-based hybrid precoding algorithm is proposed as an attractive low-complexity approach for the considered system. Using the Bussgang theorem and the additive quantization noise model (AQNM), we derive the asymptotic downlink achievable rate expressions. The rate loss caused by low-resolution DACs shows negligible quantization distortion at low power regime. Numerical results demonstrate the superiority of the proposed algorithm over other algorithms in terms of the rate performance.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

Multiple antenna technologies have attracted much research interest for several decades and have gradually made their way into mainstream communication systems. Two main benefits are adaptive beamforming gains and spatial multiplexing, leading to high data rates per user and per cell, especially when large antenna arrays are adopted. Since multiple antenna technology has become a key component of the fifth-generation (5G) networks, it is time for the research community to look for new multiple antenna technologies to meet the immensely higher data rate, reliability, and traffic demands in the beyond 5G era. Radically new approaches are required to achieve orders-of-magnitude improvements in these metrics. There will be large technical challenges, many of which are yet to be identified. In this paper, we survey three new multiple antenna technologies that can play key roles in beyond 5G networks: cell-free massive MIMO, beamspace massive MIMO, and intelligent reflecting surfaces. For each of these technologies, we present the fundamental motivation, key characteristics, recent technical progresses, and provide our perspectives for future research directions. The paper is not meant to be a survey/tutorial of a mature subject, but rather serve as a catalyst to encourage more research and experiments in these multiple antenna technologies.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

To provide high data rate for high-speed trains (HSTs), it is required to use emerging wireless communication systems, such as the fifth generation (5G). An asymmetric 5G mobile relay system is investigated for HSTs, where the mobile relay is deployed at the HST to avoid high penetration loss of the direct link between the base station (BS) and the users (TIE) inside carriages. The sub-6GHz frequency is utilized for the BS-relay link while the relay-TIE link adopts the millimeter wave frequency. Therefore, the BS-relay link experiences κ-μ fading and the relay-TIE link experiences static fluctuating two-ray fading. Moreover, the channel aging effect is considered due to the mobility of HST. For the considered system, we first study the exact statistical characterizations of the end-to-end signalto-noise ratios. Then, we derive exact closed-form expressions for key performance metrics, such as outage probability, average bit-error rate, and average achievable rate per unit bandwidth. The significant effects of channel aging, system and channel parameters on the mobile relay system are revealed from theoretical analysis and are further illustrated by simulation results. Our investigation reveals that the mobile relay system is a promising network architecture for HST communications and can provide steady and high-speed data provisioning to HST passengers against the significant bottleneck of channel aging.

In this paper, we present a comprehensive framework for the performance analysis of dual-hop relaying communications with variable gain amplify-and-forward (AF) relays and operating in the presence of both multipath fading and shadowing, modeled by the Fisher-Snedecor F-distribution. Novel closed-form expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) of the considered system subject to hardware impairments are first derived. Single-integral expressions for the numerical evaluation of the n-th moment of the end-to-end SNR, the outage probability (OP), the ergodic capacity under different adaptive transmission schemes, the effective capacity and the average bit error rate (ABER) of M-ary modulation schemes are further presented. The proposed analytical expressions are valid for most of the well-known fading distributions, provided that the moment generating function (MGF) of the inverse SNR of each hop is readily available. For the special case of ideal hardware, it is shown that the above performance metrics can be expressed in closed-form. It is worth pointing out that the proposed analysis is valid even when the destination node is equipped with multiple antennas and maximal ratio combining (MRC) is employed. The correctness of the proposed mathematical analysis is validated through extensive numerically evaluated results accompanied with Monte-Carlo simulations.

Cell-free massive multiple-input multiple-output (MIMO) is a promising network architecture for future wireless systems. This paper investigates the uplink performance of cell-free massive MIMO systems employing the least-square (LS) estimator over spatially correlated fading channels. We first derive a generalized closed-form expression of the spectral efficiency as a function of the number of access point (AP) antennas and the spatial correlation matrices. We use this result to analyze the impact that the fronthaul, number of users and number of APs have on the energy efficiency. Compared to traditional co-located massive MIMO using maximum ratio combining (MRC), our analysis shows that the large performance gain of cell-free massive MIMO with low-complexity linear LS estimators.

High-speed railway (HSR) wireless communications are required to support high data rate with seamless connectivity. In this paper, we investigate the outage performance of a downlink single-cell non-orthogonal multiple access (NOMA) based wireless network in HSR scenarios, where it is challenging to derive the perfect channel state information (CSI) and the distribution of the users are quite different from the traditional cellular scenarios. More specifically, the performance of NOMA over composite large-scale and Rician fading channel is studied with two kinds of partial CSI, e.g., imperfect small-scale CSI and no small-scale CSI. We derive the exact closed-form expression of the outage probability based on partial CSI, respectively, by using the Gauss-Chebyshev quadrature method. Finally, simulation results evidence the validity of the derived results and show that the average outage probability of NOMA systems outperforms conventional orthogonal multiple access systems.

In this paper, we investigate the performance of unmanned aerial vehicle (UAV) relaying systems with simultaneous wireless information and power transfer over the recently proposed Fisher-Snedecoer composite F fading channels. Closed form exact expressions for the outage probability (OP) and the average capacity (AC) of both decode-and-forward (DF) and amplify-and-forward (AF) protocols are derived. In the high signal-to-noise ratio region, we obtain asymptotic OP expressions to provide valuable insights into the impact of system and fading parameters on the UAV relaying performance. Finally, the accuracy of our derived expressions is validated via Monte Carlo simulations.

High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2-3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.

In this paper, we study the uplink (UL) and downlink (DL) spectral efficiency (SE) of a cell-free massive multiple-input-multiple-output (MIMO) system over Rician fading channels. The phase of the line-of-sight (LoS) path is modeled as a uniformly distributed random variable to take the phase-shifts due to mobility and phase noise into account. Considering the availability of prior information at the access points (APs), the phase-aware minimum mean square error (MMSE), non-aware linear MMSE (LMMSE), and least-square (LS) estimators are derived. The MMSE estimator requires perfectly estimated phase knowledge whereas the LMMSE and LS are derived without it. In the UL, a two-layer decoding method is investigated in order to mitigate both coherent and non-coherent interference. Closed-form UL SE expressions with phase-aware MMSE, LMMSE, and LS estimators are derived for maximum-ratio (MR) combining in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. In the DL, two different transmission modes are studied: coherent and non-coherent. Closed-form DL SE expressions for both transmission modes with MR precoding are derived for the three estimators. Numerical results show that the LSFD improves the UL SE performance and coherent transmission mode performs much better than non-coherent transmission in the DL. Besides, the performance loss due to the lack of phase information depends on the pilot length and it is small when the pilot contamination is low.

This paper inquires the achievement of secret key generation (SKG) in device-to-device (D2D) communications with the aid of relay. The confidential information between D2D users is taken under the consideration of physical layer secret key generation scheme with the help of colluding or non-colluding relay node. The selected relay conforms to help in the generation of secret keys to keep the information confidential from eavesdropping. In order to ensure the information confidential between D2D users, we explicate a mechanism for selecting relay node based on two basic social phenomena for the selection of relay node. The non-colluding relay selection is considered under the scenario of social trust, while colluding relay selection is based on social reciprocity. Furthermore, we utilize coalition game theory for the selection of optimal relay node in order to improve secret key generation rate (SKGR). Particularly, to attain more eminent SKGR within channel coherence time, the coalition game approach is determined to select an optimal node for relaying by D2D users. On the basis of relay selection, social phenomena, and coalition game theory, we propose an algorithm for achieving higher SKGR. The generated keys are not only protected from eavesdropper but also from the selected (colluding or non-colluding) relay. The performance of our proposed scheme validates and guarantees information confidentiality in D2D communications.

Providing reliable and efficient multicast services is one of the most important issues of the emerging 5G network. As the number of users grows rapidly, non-feedback multicast schemes are more suitable for bandwidth efficiency. However, conventional non-feedback multicast solutions, like the well-known Random Linear Network Coding (RLNC) and Systematic Network Coding (SNC), are suffering from complicated packet decoding and redundant packet reception at the unsaturated users. In this paper, we propose a reliable Hybrid Systematic Network Coding (HSNC) scheme to fully solve these problems. HSNC utilizes both deterministic and randomly generated coding vectors over the same finite field. By subtracting coding vectors with zero elements from a Reed- Solomon coding matrix, the goal to maximize the number of linear independent rows of a coding matrix can be easily realized. Close-form expressions of bit overhead ratio, decoding probability and computational cost are derived out. Numerical results show that HSNC, with or without a sliding window, outperforms RLNC and SNC in terms of the decoding probability. Moreover, HSNC has lower bit overhead and computational cost.

This paper studies analog beam selection schemes of discrete Fourier transform (DFT) based hybrid beamforming systems. We first derive approximations of the achievable rates when maximum-ratio combining (MRC) receiver and maximum-ratio transmitting (MRT) precoder are used in the uplink and downlink, respectively. It is shown that the achievable rate of the hybrid beamforming system is improved with the increase of the number of radio frequency chains. Also, it is found that the orthogonality condition among the line-of-sight (LoS) paths from different users directly determines the interference cancellation capability of the MRC receiver or the MRT precoder. Based on our analytical results, we propose two novel DFT beam selection schemes, referred to as exhausted searching and per-user selection. Numerical results show that the first scheme achieves higher rate while the second one is a simple suboptimal strategy with low complexity, which is practically more attractive.

Hardware impairments, such as phase noise, quantization errors, non-linearities, and noise amplification, have baneful effects on wireless communications. In this paper, we investigate the effect of hardware impairments on multipair massive multiple-input multiple-output (MIMO) two-way full-duplex relay systems with amplify-and-forward scheme. More specifically, novel closed-form approximate expressions for the spectral efficiency are derived to obtain some important insights into the practical design of the considered system. When the number of relay antennas N increases without bound, we propose a hardware scaling law, which reveals that the level of hardware impairments that can be tolerated is roughly proportional to √N. This new result inspires us to design low-cost and practical multipair massive MIMO two-way full-duplex relay systems. Moreover, the optimal number of relay antennas is derived to maximize the energy efficiency. Finally, Motor-Carlo simulation results are provided to validate our analytical results.

In this paper, we pursue a detailed outage probability analysis of multihop relaying systems taking into account the practical constraint of cochannel interferences (CCIs). Different from previous works, both relaying transmission and CCIs are performed over generalized -μ fading channels. For both independent identically and non-identically distributed -μ faded CCIs, the exact end-to-end signal-to-interference ratio expressions of the multihop relaying system are obtained. Moreover, we derive new, analytical outage probability expressions of multihop relaying systems in the presence of CCIs. In the high-power regime, simplified expressions are provided, revealing the implications of the channel parameters on the outage performance. These tractable results enable us to draw useful engineering insights about the impact of system and channel parameters on the outage probability of multihop relaying systems. Finally, simulation results are provided to validate our derived expressions.

The massive amounts of machine-type user equipments (UEs) will be supported in the future fifth generation (5G) networks. However, the potential large random access (RA) delay calls for a new RA scheme. Motivated by the key idea of non-orthogonal multiple access, the non-orthogonal random access (NORA) scheme is proposed in this paper to alleviate the access congestion problem. Specifically, NORA utilizes the difference of time of arrival to identify multiple UEs with the identical preamble, and enables power domain multiplexing of collided UEs in the following access process, while the base station performs successive interference cancellation (SIC) based on the channel conditions obtained through preamble detection. Our analysis shows that the performance of NORA is superior to the conventional orthogonal random access (ORA) scheme in terms of collision probability and throughput. Simulation results verify our analysis and further show that our NORA scheme can improve the number of the supported UEs by more than 30%. Moreover, the access delay for successfully accessed UEs are also reduced significantly by using the proposed random access scheme.

The satellite relaying system is a promising network architecture in next generation wireless communication systems, due to its large coverage and stability. In this paper, the performance of a dual-hop decode-and-forward based satellite relaying system over κ-μ shadowed fading channels is investigated, where a source earth station transmits information to a destination earth station via a satellite relay. We first obtain the exact closedform expressions of the outage probability of such a system in terms of bivariate confluent hypergeometric functions. By using a novel gamma approximation to the κ-μ shadowed distribution, the simple outage expression is derived. Moreover, the accurate expression of the average bit error rate (ABER) and the ergodic capacity are obtained in terms of Appell hypergeometric functions and hypergeometric functions, respectively. Again, with the help of the gamma approximation, we derive the approximate ABER and capacity expressions. In the high signal-to-noise ratio regime, the asymptotic ABER expression is obtained in terms of elementary functions. Finally, numerical and Monte-Carlo simulation results are provided to demonstrate the validity of the proposed unified expressions.

The practical deployment of massive multiple-input multiple-output (MIMO) in the future fifth generation (5G) wireless communication systems is challenging due to its high-hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things. Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician K-factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the tradeoff between the achievable rate and the energy efficiency with respect to key system parameters, including the quantization bits, number of BS antennas, Rician K-factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate tradeoff compared with the ideal infinite-resolution and low-resolution ADC architectures.

This paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple-input single-output (MISO) fading channels, which uses the moment generating function (MGF) based approach and H transform representation. The proposed framework has the potential to simplify the cumbersome analysis procedure compared with the probability density function-based approach. Moreover, the effective rates over two specific fading scenarios are investigated, namely, independent but not necessarily identical distributed (i.n.i.d.) MISO hyper Fox's H fading channels and arbitrary correlated generalized K fading channels. The exact analytical representations for these two scenarios are also presented. By substituting corresponding parameters, the effective rates in various practical fading scenarios, such as Rayleigh, Nakagami-m, Weibull/Gamma, and generalized K fading channels, are readily available. In addition, asymptotic approximations are provided for the proposed H transform and MGF-based approach as well as for the effective rate over i.n.i.d. MISO hyper Fox's H fading channels. Simulations under various fading scenarios are also presented, which support the validity of the proposed method.

The frequency scarcity imposed by fast growing demand for mobile data service requires promising spectrum aggregation systems. The so-called higher order statistics (HOS) of the channel capacity is a suitable metric on the system performance. While prior relevant works have improved our knowledge on the HOS characterization of spectrum aggregation systems, an analytical framework encompassing generalized fading models of interest is not yet available. In this paper, we pursue a detailed HOS analysis of κ-μ and κ-μ shadowed fading channels by deriving novel and exact expressions. Furthermore, the simplified HOS expressions for the asymptotically low and high signal-tonoise regimes are derived. Several important statistical measures, such as amount of fading, amount of dispersion, reliability, skewness, and kurtosis, are obtained by using the HOS results. More importantly, the useful implications of system and fading parameters on spectrum aggregation systems are investigated for channel selection. Finally, all derived expressions are validated via Monte Carlo simulations.

The massive amounts of machine-type user equipments (UEs) will be supported in the future fifth generation (5G) networks. However, the potential large random access (RA) delay calls for a new RA scheme and for a detailed assessment of its performance. Motivated by the key idea of non-orthogonal multiple access, the non-orthogonal RA (NORA) scheme based on successive interference cancellation (SIC) is proposed in this paper to alleviate the access congestion problem. Specifically, NORA utilizes the difference of time of arrival to identify multiple UEs with the identical preamble, and enables power domain multiplexing of collided UEs in the following access process, while the base station performs SIC based on the channel conditions obtained through preamble detection. Our analysis shows that the performance of the NORA is superior to the conventional orthogonal RA (ORA) scheme in terms of the preamble collision probability, access success probability, and the throughput of RA. Simulation results verify our analysis and further show that our NORA scheme can improve the number of the supported UEs by more than 30%. Moreover, the number of preamble transmissions and the access delay for successfully accessed UEs are also reduced significantly by using the proposed RA scheme.

In this paper, we investigate the spectral efficiency (SE) of massive multiple-input multiple-output (MIMO) systems with a large number of antennas at the base station (BS) accounting for physical space constraints. In contrast to the vast body of related literature, which considers fixed inter-element spacing, we elaborate on a practical topology in which an increase in the number of antennas in a fixed total space induces an inversely proportional decrease in the inter-antenna distance. For this scenario, we derive exact and approximate expressions, as well as simplified upper/lower bounds, for the SE of maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-squared error receivers (MMSE) receivers. In particular, our analysis shows that the MRC receiver is non-optimal for space-constrained massive MIMO topologies. On the other hand, ZF and MMSE receivers can still deliver an increasing SE as the number of BS antennas grows large. Numerical results corroborate our analysis and show the effect of the number of antennas, the number of users, and the total antenna array space on the sum SE performance.

The effective rate is an important performance metric of real-time applications in next generation wireless networks. In this paper, we present an analysis of the effective rate of multiple-input single-output (MISO) systems over α-μ fading channels under a maximum delay constraint. More specifically, novel and highly accurate closed-form approximate expressions of the effective rate are derived for such systems assuming the generalized α-μ channel model. In order to examine the impact of system and channel parameters on the effective rate, we also derive closed-form expressions of the effective rate in asymptotically high and low signal-to-noise ratio (SNR) regimes. Furthermore, connections between our derived results and existing results from the literature are revealed for the sake of completeness. Our results demonstrate that the effective rate is a monotonically increasing function of channel fading parameters α and μ, as well as the number of transmit antennas, while it decreases to zero when the delay constraint becomes stringent.

Linear precoding techniques, such as zero forcing precoding, can achieve the near-optimal capacity due to the favorable channel propagation in large-scale MIMO systems, but involve complicated matrix inversion of large size. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. We also prove that the proposed GSbased precoding scheme has a faster convergence rate than the recently proposed Neumann-based precoding scheme. Simulation results demonstrate that the proposed scheme can achieve the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations.

Free-space optical (FSO) communications can achieve high capacity with huge unlicensed optical spectrum and low operational costs. The corresponding performance analysis of FSO systems over turbulence channels is very limited, particularly when using multiple apertures at both transmitter and receiver sides. This paper aims to provide the ergodic capacity characterization of multiple-input-multiple-output (MIMO) FSO systems over atmospheric turbulence-induced fading channels. The fluctuations of the irradiance of optical channels distorted by atmospheric conditions is usually described by a gamma-gamma (rr) distribution, and the distribution of the sum of rr random variables (RVs) is required to model the MIMO optical links. We use an α - μ distribution to efficiently approximate the probability density function (pdf) of the sum of independent and identical distributed ΓΓ RVs through moment-based estimators. Furthermore, the pdf of the sum of independent, but not necessarily identically distributed ΓΓ RVs can be efficiently approximated by a finite weighted sum of pdfs of ΓΓ distributions. Based on these reliable approximations, novel and precise analytical expressions for the ergodic capacity of MIMO FSO systems are derived. Additionally, we deduce the asymptotic simple expressions in high signal-to-noise ratio regimes, which provide useful insights into the impact of the system parameters on the ergodic capacity. Finally, our proposed results are validated via Monte Carlo simulations.

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

The Gallager's random coding error exponent for space-time block codes (STBC) over multiple-input multiple-output (MIMO) block-fading channels, with Gaussian input distribution, is investigated. Gallager's error exponent can be used to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We first provide new, analytical expressions for Gallager's exponent of STBC systems over η-μ fading channels. The Shannon capacity and cutoff rate, which can be directly derived from Gallager's exponent, are further examined. In order to get additional insights, a high signal-to-noise ratio analysis is pursued to investigate the effects of coherence time and codeword length on the error probability. For the sake of completeness, we provide the link to previous known results on Rayleigh and Nakagami-m fading channels.

In this paper, we analytically investigate the achievable rate of multiple-input single-output (MISO) channels in the presence of delay constraints. In particular, we focus on the so-called effective rate which was recently established as a suitable metric for assessing the impact of delay constraints on the overall performance of communication systems. Yet, most prior relevant works have considered only the typical cases of Rayleigh, Rician and Nakagami-m fading which allow for tractable manipulations. In this paper, we relax this assumption by considering MISO systems over η-μ fading channels. The η-μ distribution has been shown to provide very good fit to experimental data in various propagation environments. New, analytical expressions for the exact effective rate are derived. Moreover, we consider the asymptotically high and low signal-to-noise (SNR) regimes, for which tractable, closed-form effective rate expressions are presented. These results enable us to explicitly investigate the impact of system parameters on the effective rate of MISO η-μ fading channels.

In this paper, we analytically investigate Gallager's exponent for space-time block codes over multiple-input multiple-output block-fading channels with Gaussian input distribution. As a suitable metric of the fundamental tradeoff between communication reliability and information rate, Gallager's exponent can be used to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We assume that the receiver has full channel state information (CSI), while the transmitter has no CSI and performs equal power allocation across all transmit antennas. In the following, novel exact expressions for Gallager's exponent are derived for two well-known channel fading models, namely η-μ and κ-μ fading models. More importantly, the implications of fading parameters and channel coherence time on Gallager's exponent are investigated. In addition, we present new expressions for the Shannon capacity, cutoff rate and expurgated exponent for the above mentioned fading models, while in the high signal-to-noise ratio regime, simplified closed-form expressions are also derived. Finally, we highlight the fact that the presented analysis encompasses all previously known results on Nakagami-m, Rician, Rayleigh and Hoyt fading channels, as special cases.

In this paper we exploit the benefits of the diversity gains arising from a cluster of opportunistic relays (OR) and from the independently fading subcarriers of multiple users. Our goal is to improve the energy-efficiency of the OR assisted single-carrier frequency-division multiple-access (SC-FDMA) uplink using amplify-and-forward (AF), where the direct transmission (DT) link is unavailable. By assuming that the pilot aided channel quality information (CQI) of all the users may be exchanged amongst the cooperating relays, we propose two joint dynamic resource allocation (DRA) schemes based on the so-called `first-hop quality awareness'. Our results demonstrate that compared to the DT benchmark, the proposed joint DRA schemes are capable of achieving a power reduction of 10dB for a single-antenna base station (BS) receiver, albeit for a multi-antenna BS the power-reduction remains more modest.

In this paper, we investigate the achievable performance of channel coded single-carrier frequency-division multiple-access (SC-FDMA) systems employing various detection schemes, when communicating over frequency-selective fading channels. Specifically, three types of minimum mean-square error (MMSE) based frequency-domain (FD) turbo equalisers are considered. The first one is the turbo FD linear equaliser (LE). The second one is a parallel interference cancellation (PIC)-assisted turbo FD decision-feedback equaliser (DFE). The final one is the proposed hybrid interference cancellation (HIC)-aided turbo FD-DFE, which combines successive interference cancellation (SIC) with iterative PIC and decoding. The benefit of interference cancellation (IC) is analysed with the EXtrinsic Information Transfer (EXIT) charts. The performance of the coded SC-FDMA systems employing the above-mentioned detection schemes is investigated with the aid of simulations. Our studies show that the IC techniques achieve an attractive performance at a moderate complexity.

In this paper, a multiple input multiple output (MIMO) scheme with spare antenna array is investigated for the case of high-speed railway scenario. Using a recently proposed criterion for maximizing MIMO system capacity, the antenna elements are designed to achieve subchannel orthogonality and hence the channel response is not rank deficient. Theoretical and numerical results validate that the MIMO scheme proposed for railway environment is seen to achieve a significant capacity enhancement in the high regime of Ricean K factor. The performance of this MIMO system is robust under the implications of the model parameters, such as scattering channel and railway curvature effects.

With the rapid development of high-speed railways all over the world, it is very promising to deliver public broadband access to passengers aboard high-speed trains. However, it has been a challenge to support high speed (e.g. above 350 km/h) mobile users with broadband communications, because of rapidly fading radio channels and Doppler frequency shift. This paper presents a review of network architectures providing broadband access to high-speed trains. A novel scheme referred as multimode multi-band and multi-system-based access architecture is proposed in this paper. It consists of three parts: (1) train network part that relays data to large number of users in carriages without changing their own terminals, (2) ground network part that deals with traffic from different operators' networks, and (3) ground-to-train network part that connects the train network and ground network. Moreover, this architecture could support high mobility with broad bandwidth, seamless handover, scalable and cost-effective deployment.

In this contribution we exploit the benefits of combining the diversity gains arising from cooperation, multiple propagation paths and from the opportunistic scheduling of multiple users, for the sake of supporting a power-efficient single-relay assisted single-carrier frequency-division multiple-access uplink, where each relay supports a single user communicating over dispersive channels subject to large-scale fading. Based on the proposed joint frequency-domain equalisation and diversity combining aided receiver relying on the minimum mean-square error criterion, two different relay selection schemes designed for single-user and multi-user scenarios are investigated, which employ imperfect power control. Our results show that at a bit error ratio of 10-4, the proposed receiver is capable of saving 2 dB power by achieving a higher cooperative diversity gain than the conventional receiver. Moreover, an approximately 6.8 dB and 8 dB transmit power reduction are attainable by invoking the proposed selective diversity oriented single-user and multi-user relay selection schemes, respectively. Most importantly, a power-efficiency improvement is gleaned from our multi-user relay selection scheme, which avoids the effects of deep shadowing and provides 9.7dB power reduction compared to the non-cooperation scenario.

This paper characterises two single-relay assisted single-carrier frequency-division multiple-access (SC-FDMA) amplify-and-forward (AF) based cooperative strategies employed for multiuser uplink transmissions over multipath fading channels. For the sake of exploiting the benefits of cooperative diversity, we investigate both single-dedicated-relaying (SDR) as well as single-shared-relaying (SSR), and propose a frequency-domain (FD) subband-based AF scheme combined with subband remapping, which is benchmarked against conventional time-domain (TD) AF in the context of both of the above-mentioned relaying topologies. Our simulation results show that cooperative diversity is beneficial for both SDR and SSR, both in the absence and in the presence of multipath diversity. More importantly, the proposed FD-AF scheme is capable of attaining a multiuser performance, which is better than that of the conventional single-user TD-AF protocol operating in a multipath environment, because of the subband-based AF and noise suppression capability of the relay.