Unmanned aerial vehicles (UAVs) are emerging as a promising platform for enabling integrated sensing and communication (ISAC) in multi-cell systems due to their deployment flexibility. However, this flexibility also introduces significant challenges, particularly co-channel interference at the UAV receiver. In this paper, we propose a novel adaptive co-channel interference mitigation model for UAV-enabled multi-cell systems. Specifically, the proposed model consists of two key components: a cost function and an update algorithm. First, we derive a new cost function that incorporates both magnitude and phase errors–critical metrics for guiding the estimated signal toward the desired signal. Second, the cost function is extended to formulate a parameter update algorithm, whose effectiveness is analyzed using both geometric and entropy-based approaches. Simulation results demonstrate that the proposed method outperforms state-of-the-art techniques, establishing it as a robust solution for interference mitigation in UAV-enabled multi-cell ISAC systems.

Fluid antenna systems (FAS) have recently emerged as a promising solution for sixth-generation (6G) ultra-dense connectivity. These systems utilize dynamic radiating and/or shaping techniques to mitigate interference and improve spectral efficiency without relying on channel state information (CSI). The reported improvements achieved by employing a single dynamically activated radiating position in fluid antenna multiple access (FAMA) are significant. To fully realize the potential of FAMA in multi-user multiplexing, we propose leveraging the unique fast-switching capabilities of a single radio-frequency (RF)-chain meta-fluid antenna structure to achieve multi-activation. This allows for a significantly larger set of independent radiating states without requiring additional signal processing. Simulations demonstrate that multi-activation FAMA enables robust multi-user multiplexing with a higher signal-to-interference ratio (SIR) under various Rayleigh-fading environments compared to other single RF-chain technologies. We further show that the SIR can be optimized within a 15~$μs$ timeframe under a multi-user Rayleigh-fading channel, making the proposed scheme highly suitable for fast-changing wireless environments. Verified through the theoretical Jakes' model, full three-dimensional (3D) electromagnetic (EM) simulations and experimental validation, multi-activation FAMA enables effective CSI-free, multi-user communication, offering a scalable solution for high-capacity wireless networks.

Smart cities demand versatile antenna systems supporting heterogeneous wireless applications across diverse propagation environments. This paper presents a wideband pixel-based fluid antenna system (PB-FAS) designed as a general-purpose antenna solution for smart city infrastructures, addressing fundamental challenges in wideband operation, spatial adaptability, interference mitigation, and scalable deployment. The proposed PB-FAS integrates parasitic elements for enhanced bandwidth (6.0-7.0 GHz) and a compact 6-PIN-diode pixel surface enabling 64 distinct fluid states, achieving optimal cost-performance balance. An integrated FPGA-based control system provides microsecond-level reconfiguration for real-time channel adaptation. We establish a rigorous exact spatial geometry (ESG) channel model capturing state-dependent antenna responses across near-field and far-field regions, providing a unified theoretical foundation for interference mitigation analysis. Comprehensive validation through full-wave electromagnetic simulations, anechoic chamber measurements, and experimental two-source 16-QAM communication tests demonstrates up to 11 dB SINR improvement and 13.2% EVM reduction through hardware-level spatial diversity, confirming the system’s effectiveness as a scalable, cost-effective solution for next-generation smart city wireless infrastructures ranging from IoT sensor networks to high-capacity backhaul links.

Mobile edge computing–space-air-ground integrated network (MEC-SAGIN) is emerging as a crucial component of future wireless systems. Despite its potential, addressing network fluctuations while ensuring continuous low-latency computing services in highly dynamic environments remains a significant challenge. To address this issue, this paper proposes a fluid antenna (FA)-assisted MEC-SAGIN system, which enhances channel transmission conditions and reduces uplink task offloading latency by flexibly adjusting the antenna ports of edge computing users equipped with FAs. Specifically, we aim to minimize the maximum total computational delay (TCD) of edge computing tasks for ground users (GUs) and the satellite user (SU) by jointly optimizing the task offloading strategies, computational resource allocation, FA port positions, unmanned aerial vehicle (UAV) location, and the receive beamforming matrix. To solve this non-convex problem, we employ the block coordinate descent (BCD) technique to decompose the original problem into four subproblems. The subproblems are optimized using a combination of low-complexity iterative algorithms and the projected gradient descent (PGD) method to refine communication and computation configurations as well as FA port selection. Simulation results demonstrate that the FA-assisted scheme significantly improves the TCD performance of the MEC-SAGIN system. It maintains transmission stability and reliability in dynamic environments while outperforming conventional fixed-position antennas (FPAs) and random-port antenna schemes.

Reconfigurable intelligent surfaces (RIS) have emerged as a crucial technology for making sustainable and green communication in future sixth-generation (6G) networks. By adaptively adjusting the wireless environment, RIS facilitates flexible control over signal propagation. When RIS is integrated into a simultaneous wireless information and power transfer (SWIPT) system, it can improve energy efficiency (EE) and link reliability. This enables low-power internet-of-things (IoT) devices to achieve continuous energy acquisition while maintaining reliable data access. This capability aligns well with the increasing demands of future 6G networks for enhancing EE and achieving ubiquitous connectivity. Motivated by this, we investigate a RIS-assisted multi-user SWIPT system that accounts for the nonlinear characteristics of the high power amplifier (HPA) at the transmitting end and the nonlinearity of the energy harvesting (EH) circuits at the receivers. These hardware imperfections pose major challenges for system modeling and optimization, particularly under stringent power limitations and quality of service (QoS) requirements, where improving overall EE is a key objective. Therefore, we proposed a joint optimization framework that simultaneously designs the access point (AP) beamforming, the RIS reflection coefficients, and the power splitting (PS) ratios at the receivers. The resulting problem is non-convex, with strong coupling among these variables. To tackle this issue, we propose an efficient alternating optimization (AO) framework. The fractional EE objective is handled using the Dinkelbach method, while each subproblem is iteratively convexified via successive convex approximation (SCA) and semi-definite relaxation (SDR) algorithms. Simulation results reveal that the proposed AO approach achieves substantial EE gains and confirm that integrating RIS conspicuous boosts the overall system EE performance.

Stacked intelligent metasurfaces (SIM) have become a promising technology to improve the wave-domain signal processing and increase the wireless communication capacity. However, optimizing the phase configuration remains a significant challenge due to the discrete and highly combinatorial nature of the multi-layer architecture. To address this, we propose a quantum-inspired coordinated design framework for joint wave-based beamforming and power allocation in SIM-assisted multiuser systems. By leveraging a black-box second-order approximation, the discrete phase optimization is reformulated into a standard quadratic unconstrained binary optimization (QUBO) problem. Quantum-inspired discrete simulated bifurcation (dSB) solver is used to find the candidates effectively, and then a tabu-based local refinement strategy is applied to refine these candidates and reduce the deviation of the approximate solution. Concurrently, an iterative water-filling scheme is integrated to optimize power allocation, facilitating a synergy between global search and fine-grained control. Simulation results confirm that the proposed approach consistently outperforms classical benchmarks in terms of sum rate, convergence speed, and interference suppression. The framework exhibits strong scalability across varying system dimensions and channel realizations, validating its effectiveness in wave-domain communication scenarios.

In covert communications, the position of the warden has a variety of situations, which leads to different scenes that require different covert communication schemes to ensure the security. In response to this situation, in this paper, a beamforming optimization method of covert communications for multi-position warden is proposed. Firstly, we formulate a general optimization problem and optimize it to maximize the covert communication rate of the user based on Dinkelbach’s transform. Subsequently, according to the optimized general optimization problem, we propose three schemes for three scenes corresponding to different fixed warden positions, using appropriate technologies for assistance in each scheme. Specifically, the intelligent reflecting surface (IRS) is used in Scene 1 and the integrated communication and jamming (ICAJ) is used in Scenes 2 and 3, and these technologies can assist the covert communication. Moreover, we propose an alternate optimization (AO) algorithm to solve the optimization problem of Scene 1 for its optimal covert communication performance. Additionally, we also propose an AO algorithm to solve the optimization problems of Scenes 2 and 3 to optimize the active beamforming. Simulation results demonstrate the effectiveness of all three proposed schemes, that outperform their respective benchmark schemes.

Sixth-generation (6G) networks are rapidly becoming a focal point of global technological innovation, driven by the need to support hyper-reliable, low-latency, and intelligent connectivity for applications such as immersive extended reality, autonomous systems, and ubiquitous sensing. While 6G promises transformative advancements in wireless communication, achieving its ambitious goals poses significant fundamental challenges. One natural direction is to scale multiple-input multiple-output (MIMO) technology to unprecedented levels; however, doing so introduces substantial hardware complexity and power consumption. To overcome these limitations, recent research has explored antenna reconfigurability as a novel degree of freedom (DoF) at the physical (PHY) layer. Among these efforts, the fluid antenna system (FAS) has emerged as a compelling concept, offering reconfigurability in both spatial positioning and physical structure. This idea has inspired related innovations, including movable antennas, flexible-position MIMO, reconfigurable MIMO architectures, and adaptive antenna arrays, collectively referred to as next-generation reconfigurable antenna (NGRA) systems. While prior work has primarily focused on spatial flexibility, this article introduces a generalized model of FAS that incorporates both structural and morphological fluidity, enabling the vision of “shapeless and formless” antennas in future wireless systems. We analyze FAS’s potential to enhance key performance metrics such as coverage, energy efficiency, reliability, and spectral capacity. In addition, we outline implementation challenges and explore synergies with key 6G enablers, including reconfigurable intelligent surfaces (RIS), non-terrestrial networks (NTN), integrated sensing and communication (ISAC), and artificial intelligence (AI). This survey provides a comprehensive overview of NGRA systems and identifies promising directions for future research in reconfigurable wireless technologies.

Semantic communication (SC) systems face the challenge of completing generation and comprehension tasks simultaneously under high data compression. To address this issue, this paper proposes a You Only Transmit Once (YOTO) SC system, which achieves efficient performance by synergistically integrating images and texts. For generation tasks, YOTO integrates a multiple granularity visual encoder, a digital modulation autoencoder, and a conditional probability-based visual decoder. These parts collectively ensure high-fidelity image reconstruction and accurate object detection. Meanwhile, YOTO utilizes the Source Feature Selection (SFS) and Channel Feature Selection (CFS) module for joint compression of source and channel coding data. To achieve a globally optimal compression, the Libra module dynamically adjusts feature retention ratios between CFS and SFS. Regarding comprehension tasks, YOTO eliminates the need for additional text data transmission by simply replacing the visual decoder with a Multimodal Language Model that incorporates the visual causality and bridging module. This architecture effectively converts demodulated visual data into descriptive captions while enabling emotion-aware text generation through prompt engineering. Extensive simulation experiments demonstrate the superior performance of YOTO in diverse channel conditions. In generation tasks, the YOTO system achieves PSNR values exceeding 30 dB both in the AWGN and Rayleigh fading channels when the SNR is 6 dB. While in comprehension tasks, it attains sentence similarity scores above 0.83 at 10 dB SNR. YOTO system effectively resolves cross-modal semantic disparities, thereby paving the way for future developments of SC systems that can balance generation and comprehension tasks.

This paper explores the unsourced/uncoordinated random access (URA) problem for integrated sensing and communication (ISAC) systems. Recent findings indicate that conventional multiple access strategies, such as treating interference as noise (TIN) and time-division multiple access (TDMA), are often overwhelmed and fail to support the rapidly increasing number of active users. To address this, the unsourced ISAC (UNISAC) system model has emerged as a promising framework for future ISAC networks. In this work, we adopt a realistic channel model and propose the use of a fluid antenna system (FAS) for UNISAC. We derive the achievable performance bounds and floor for the proposed FAS-UNISAC to demonstrate its significant potential. For communication, the randomized overlapping of channel responses from different scattering paths yields substantial gains within a compact antenna space.We prove that the joint detection and decoding error is inversely proportional to the channel gain. For line-of-sight (LOS) only channel model, leveraging covariance information through the spatial diversity of FAS effectively expands the receiving aperture, thereby enhancing sensing resolution. A universal sensing upper bound is established as a benchmark for potential sensing methods. Additionally, we investigate the benefits of asynchronous transmission, which introduces an extra degree of freedom through resource multiplexing gains via randomized timing offsets among users. Our results show that the spatial diversity inherent in FAS significantly enhances the supported user volume, as well as the sensing and communication capabilities.

This paper considers communication between a base station (BS) to two users, each from one side of a simultaneously transmitting-reflecting reconfigurable intelligent surface (STAR-RIS) in the absence of a direct link. Rate-splitting multiple access (RSMA) strategy is employed and the STAR-RIS is subjected to phase errors. The users are equipped with a planar fluid antenna system (FAS) with position reconfigurability for spatial diversity. First, we derive the distribution of the equivalent channel gain at the FAS-equipped users, characterized by at-distribution. We then obtain analytical expressions for the outage probability (OP) and average capacity (AC), with the latter obtained via a heuristic approach. Our findings highlight the potential of FAS to mitigate phase imperfections in STAR-RIS-assisted communications, significantly enhancing system performance compared to traditional antenna systems (TAS) with only modest hardware complexity and negligible training or feedback overhead at the user side. Furthermore, we quantify the impact of practical phase errors on system efficiency, emphasizing the importance of robust and energy-efficient strategies for next-generation wireless networks.

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.

As sixth-generation (6G) wireless networks demand unprecedented connectivity and interference management capabilities, rate-splitting multiple access (RSMA) emerges as a promising solution through common and private stream partitioning and remains effective across a range of channel state information at the transmitter (CSIT) qualities and traffic heterogeneity. In practical multiuser deployments, two considerations arise: the common stream decoding constraint imposed by the weakest user, and residual inter-user interference can remain non-negligible-particularly in single-input single-output (SISO) broadcast settings and under an imperfect CSIT scenario. Motivated by prior advances of RSMA research, we investigate a complementary mechanism-fluid antenna systems (FAS) with dynamic port reconfiguration-that supplies adaptive spatial selectivity without altering the RSMA signaling structure. Can FAS help alleviate these considerations and enhance RSMA performance? This paper demonstrates that dynamic port reconfiguration in FAS provides adaptive spatial selectivity that can strengthen the weakest user’s effective channel, improve signal-to-interference-plus-noise (SINR) ratios through enhanced channel gains and reduced relative noise impact. We develop a tractable, correlation-aware analytical framework that captures realistic spatial dependencies through advanced block-correlation modeling, considering both constant block correlation (CBC) and variable block correlation (VBC) variants. Our analysis yields closed-form expressions for outage probability (OP) and ergodic rate (ER) that quantify the impact of FAS on RSMA performance. Extensive simulations validate our theoretical findings: VBC-based results exhibit consistently tighter agreement with Monte Carlo simulations than CBC across all port configurations. Moreover, FAS-RSMA achieves enhancing performance gains over traditional fixed-position antenna (FPA) and non-orthogonal multiple access (NOMA), demonstrating lower OP and substantially higher ER through the synergy of RSMA’s flexible interference management and FAS’s adaptive spatial diversity.

Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

Pixel antennas, based on discretizing a continuous radiation surface into small elements called pixels, are a flexible reconfigurable antenna technology. By controlling the connections between pixels via switches, the characteristics of pixel antennas can be adjusted to enhance the wireless channel. Inspired by this, we propose a novel technique denoted antenna coding empowered by pixel antennas. We first derive a physical and electromagnetic based communication model for pixel antennas using microwave multiport network theory and beamspace channel representation. With the model, we optimize the antenna coding to maximize the channel gain in a single-input single-output (SISO) pixel antenna system and develop a codebook design for antenna coding to reduce the computational complexity. We analyze the average channel gain of SISO pixel antenna system and derive the corresponding upper bound. In addition, we jointly optimize the antenna coding and transmit signal covariance matrix to maximize the channel capacity in a multiple-input multiple-output (MIMO) pixel antenna system. Simulation results show that using pixel antennas can enhance the average channel gain by up to 5.4 times and channel capacity by up to 3.1 times, demonstrating the significant potential of pixel antennas as a new dimension to design and optimize wireless communication systems.

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

This paper proposes a novel covert transmission framework for an unmanned aerial vehicle (UAV)-reconfigurable holographic surface (RHS)-aided full-duplex (FD) integrated sensing and communication (ISAC) system, where the aerial access point (AP) simultaneously performs target sensing and downlink covert communication. We jointly design the AP’s downlink transmit signal and uplink receive beamformers, the RHS weights, the users’ uplink transmit powers, and the UAV’s trajectory, considering imperfect knowledge of the warden’s channel state information (CSI). An optimization problem is formulated to maximize the minimum covert transmission rate (CTR) among all downlink covert users (DCUs), subject to constraints on required sensing and uplink transmission capabilities, covertness, and total power budget. To tackle the intractable non-convex problem, we leverage the Bernstein-type inequality, majorization-minimization (MM), and successive convex approximation (SCA), and propose a secure optimization framework that efficiently updates all variables using convex optimization techniques. To further understand the proposed algorithm, its convergence behavior and computational complexity are discussed. Simulation results demonstrate that integrating RHS and UAV techniques into the optimization design enhances the covert transmission performance of FD-ISAC systems while ensuring a certain level of sensing capability.

In this paper, we contribute to the beamforming design problem with imperfect channel state information in a monostatic integrated sensing and communication (ISAC) system. We propose a robust waveform design framework tailored for monostatic ISAC systems, incorporating a channel random error vector to account for imperfections in the communication channels, and addressing clutter interference in radar sensing received waveforms. Next, we derive expressions for target estimation performance via utilizing the minimum mean squared error criterion and the Cramer-Rao bound, which serve as objective functions for our beamforming design. Additionally, we introduce signal-to-interference-plus-noise ratio outage probability constraints and power constraints, formulating two different beamforming optimization problems. Utilizing semi-definite programming techniques, we reformulate these optimization problems into convex optimization problems and resolve them via a convex toolbox. Finally, our simulation results achieves 45% sensing gain and 37% communication gain at an SINR threshold of 20 dB compared with the baseline.

Integrated sensing and communication (ISAC) is regarded as a promising solution for the development of the emerging low-altitude economy (LAE). Given the necessity of accurate and realistic wireless channel models for ISAC evaluation and optimization, this paper proposes an LAE-oriented ISAC channel modeling framework. By incorporating the target unmanned aerial vehicle (UAV) scattering response, we decouple the ISAC channel into target and background channels. For the target channel, the model is formulated as a cascade of the transmitting base station (BS)-target link, the target-sensing BS link, and the scattering response from the target UAV. For the background channel, a novel parameter is introduced to separate the influence of the target UAV. Furthermore, key statistical properties, including the space-time-frequency correlation function, coherence distance, Doppler power spectral density, root mean square delay spread, and stationarity interval, are derived and analyzed. The accuracy of the proposed channel model is verified by the close agreement between simulated statistics properties and measured data, and the effectiveness of the cascaded method is validated by the close agreement between the concatenation output and simulation results.

Fluid antenna system (FAS) technology can further improve the performance by changing the antenna position and shape over a given space dynamically. In this paper, a FAS-assisted orbital angular momentum (FAS-OAM) communication system is proposed, in which the base station (BS) transmits OAM signals with multiple modes to a receiver equipped with fluid antennas. In order to analyze the performance of the proposed system accurately, the block-correlation model is employed to construct the channel correlation of FAS with low complexity. Then, the outage probability and ergodic capacity are derived based on the assumption of the non-central chi-square distribution, and the Gauss-Laguerre quadrature method is adopted to obtain closed-form results. Simulation results show that the derived theoretical approximate and closed-form results of outage probability and ergodic capacity are consistent with corresponding simulation results. Compared with the conventional OAM and MIMO systems, the proposed FAS-OAM system exhibits significant performance advantages in terms of outage probability and ergodic capacity, and confirms the effectiveness of FAS-OAM over fading channels.

The advent of extremely large antenna arrays and high-frequency signaling is expected to enable next-generation integrated sensing and communication (ISAC) networks to predominantly operate in the near-field region. Due to the dual influence of distance and angle on wave propagation characteristics in the near-field region, accurately modeling these characteristics remains a critical challenge. Motivated by the potential of large language models (LLMs) in angle prediction and distance estimation, an LLM-enhanced multi-objective optimization problem (MOOP) is developed to accurately capture the dependence of the channel on both the angular position and distance. The formulated LLM-enhanced MOOP framework is decomposed into a series of sub-problems, which can balance spectral efficiency for communication and localization accuracy for sensing. To overcome the computational and energy challenges associated with LLMs, a gray wolf optimization (GWO)-based algorithm is integrated as black-box search operator with LLM-specific prompt engineering to solve these sub-problems. Numerical results demonstrate that the proposed LLM-GWO scheme achieves an trade-off between communication and sensing performance, outperforming baseline approaches in terms of both Pareto front quality and convergence.

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

The unmanned/uncrewed aerial vehicle (UAV) assisted mobile edge computing (MEC) technology has become a viable and flexible solution for providing computation offloading and energy charging services for ground users, especially in scenarios with terrible direct links. Therefore, latency has become one of the crucial design issues subject to the energy limitations of the UAV and users. Motivated by this, we study a latency-aware air ground hybrid MEC system with an assistant UAV and a ground base station (GBS) to serve and charge multiple users under both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols. The task completion latency minimization problems are formulated by jointly optimizing the time slot scheduling, CPU frequency allocation, UAV's three dimensional (3D) trajectory design, transmit power allocation, as well as the number of required time slots. To address the formulated mixed integer non-convex optimization problems, we introduce an efficient alternating optimization algorithm with a double-loop structure. In the outer loop, we constantly adjust the number of time slots by employing the bisection search method and determine the search range via feasibility check. In the inner loop, we first transform the original subproblem into an equivalent problem that maximizes the minimum computation completion ratio of the users. Then we further deconmpose this transformed problem into four subproblems, which can be solved by a proposed iterative algorithm. Extensive experiments are con ducted to illustrate the efficacy and superiority of the proposed algorithm over the other benchmark schemes in minimizing the task completion latency, particularly in scenarios where the computing resource is limited or the density of users is high.

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.

We investigate antenna coding utilizing pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. The objective is to enhance the output direct current (DC) power under RF combining and DC combining schemes by jointly exploiting gains from antenna coding, beamforming, and rectenna nonlinearity. We first propose the MIMO WPT system model with binary and continuous antenna coding using the beamspace channel model and formulate the joint antenna coding and beamforming optimization using a nonlinear rectenna model. We propose two efficient closed-form successive convex approximation algorithms to efficiently optimize the beamforming. To further reduce the computational complexity, we propose codebook-based antenna coding designs for output DC power maximization based on K-means clustering. Results show that the proposed pixel antenna empowered MIMO WPT system with binary antenna coding increases output DC power by more than 15 dB compared with conventional systems with fixed antenna configuration. With continuous antenna coding, the performance improves another 6 dB. Moreover, the proposed codebook design outperforms previous designs by up to 40% and shows good performance with reduced computational complexity. Overall, the significant improvement in output DC power verifies the potential of leveraging antenna coding utilizing pixel antennas to enhance WPT systems.

Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement. This paper explores the integration of a transmit FAS array for hybrid beamforming (HBF) into a reconfigurable intelligent surface (RIS)-assisted communication architecture for multiuser communications in the downlink, corresponding to the downlink RIS-assisted multiuser multiple-input single-output (MISO) FAS model (Tx RIS-assisted-MISO-FAS). By considering Rician channel fading, we formulate a sum-rate maximization optimization problem to alternately optimize the HBF matrix, the RIS phase-shift matrix, and the FAS position. Due to the strong coupling of multiple optimization variables, the multi-fractional summation in the sum-rate expression, the modulus-1 limitation of analog phase shifters and RIS, and the antenna position variables appearing in the exponent, this problem is highly non-convex, which is addressed through the block coordinate descent (BCD) framework in conjunction with semidefinite relaxation (SDR) and majorization-minimization (MM) methods. To reduce the computational complexity, we then propose a low-complexity grating-lobe (GL)-based telescopic-FA (TFA) system with multiple delicately deployed RISs under the sub-connected HBF architecture and the line-of-sight (LoS)-dominant channel condition, to allow closed-form solutions for the HBF and TFA position. Our simulation results illustrate that the former optimization scheme significantly enhances the achievable rate of the proposed system, while the GL-based TFA scheme also provides a considerable gain over conventional fixed-position antenna (FPA) systems, requiring statistical channel state information (CSI) only and with low computational complexity.

Integrated sensing, communications, and computation (ISCC) is becoming increasingly critical, particularly for enabling advanced intelligent applications. This paper proposes an ISCC framework for edge-intelligent networks, where edge intelligent devices (EIDs) cooperatively sense multiple mobile targets and simultaneously offload radar sensing data to a base station (BS) equipped with an edge server for processing. To address the time-varying nature of the network, we develop an online resource management strategy that maximizes the long-term average weighted sum rate (AWSR), subject to queue stability, average power constraints, and quality-of-service (QoS) requirements. Using the Lyapunov drift-plus-penalty framework, the original stochastic optimization problem is decomposed into a sequence of deterministic subproblems across time slots. At each time slot, sensing scheduling, transmit beamforming for both sensing and communications, receive beamforming for radar echoes, and computing resource allocation at the BS are jointly optimized through an efficient alternating optimization algorithm based on the current system state. Simulation results validate the effectiveness of the proposed online strategy, showing superior performance over baseline methods and revealing the influence of key parameters. In particular, a trade-off is observed between the AWSR and queue backlogs, which can be flexibly tuned via control parameters.

The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.

An auto-polarization fluid antenna (APFA) is developed for indoor wireless channel sounding and employed to reveal a novel “fluid polarization effect” (FPE) in wireless communications. Unlike conventional fluid antennas (FAs) that are steering their beams/nulls with the aid of external mechanical/electronic actuators, the APFA only relies on the natural swinging of human arms to yield a self-driven polarization switching ability. Compared with the conventional fixed circularly polarized antennas, the wrist-worn, self-driven APFA in indoor wireless channel sounding systems effectively reduces multipath clusters (MPCs), attains smaller path loss exponent (PLE), and consequently yields the FPE. Compared to the fixed circularly polarized case with PLE= 1.62, the measured PLE is reduced by 14% to 1.38, and the system packet error rate (PER) is improved by 76%. It realizes robust anti-multipath fading performance owing to the much-improved FPE. The fluid effect in polarization domain is anticipated to remarkably enhance the anti-multipath fading performance of wearable wireless communication systems. It opens a new horizon to develop self-driven, cost-effective fluid antenna systems (FASs) for universal applications.

In-band full-duplex (IBFD) systems are expected to double the spectral efficiency compared to half-duplex systems, provided that loopback self-interference (SI) can be effectively suppressed. The inherent interference mitigation capabilities of the emerging fluid antenna system (FAS) technology make it a promising candidate for addressing the SI challenge in IBFD systems. This paper thus proposes a FAS-assisted self-interference cancellation (SIC) framework, which leverages a receiver-side FAS to dynamically select an interference-free port. Analytical results include a lower bound and an approximation of the residual SI (RSI) power, both derived for rich-scattering channels by considering the joint spatial correlation amongst the FAS ports. Simulations of RSI power and forward link rates validate the analysis, showing that the SIC performance improves with the number of FAS ports. Additionally, simulations under practical conditions, such as finite-scattering environments and wideband integrated access and backhaul (IAB) channels, reveal that the proposed approach offers superior SIC capability and significant forward rate gains over conventional IBFD SIC schemes.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. Unlike traditional fixed-position multiple-input multiple-output (MIMO) systems, a FAS possesses position reconfigurability to switch on-demand amongNpredefined ports over a prescribed space. This paper explores the performance of a single-input single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closed-form bounds for the outage probability of the Rx-SISO-FAS model, as well as exact and closed-form lower bounds for the ergodic rate. Importantly, we also analyze the performance considering both uniform linear array (ULA) and uniform planar array (UPA) configurations for the ports of the FAS. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,N, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:i) the Rx-SISO-FAS model can enhance performance under spatially-correlated Rician fading channels over the fixed-position antenna counterpart;ii) the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime;iii) FAS can outperform anLbranches maximum ratio combining (MRC) system under Rician fading channels; andiv) when the number of ports is identical, UPA outperforms ULA.

This paper introduces a unified analytical and optimization framework for fluid antenna system-active reconfigurable intelligent surface (FAS-ARIS) communications in 6G. By combining the port reconfigurability of FAS with the signal amplification of ARIS, the proposed design enables more flexible control of the propagation environment and enhanced link reliability beyond what passive solutions can offer. We first derive the optimal ARIS amplification gain under a reflection power constraint to maximize the user’s signal-to-noise ratio (SNR). Using a block-diagonal matrix approximation, we obtain a tractable outage expression and a tight independent-antenna equivalent upper-bound. Building on this, we establish the monotonic relationship between outage and effective channel gain, which enables a closed-form solution for ARIS phase optimization under limited channel state information (CSI). To further improve spectral efficiency, we propose a region-partitioned throughput optimization framework that achieves near-optimal performance without exhaustive search, thereby verifying its low computational complexity. Extensive simulations confirm the accuracy of the analysis and demonstrate consistent gains in outage and throughput compared to baselines.

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.

Faster-than-Nyquist (FTN) technique promises higher capacity and spectral efficiency for wireless communications. However, existing FTN studies over doubly-selective fading (DSF) channels separate channel-induced inter-symbol interference (channel-ISI) and FTN-induced ISI (FTN-ISI) to simplify cancellation. In practical DSF scenarios, the inherent coupling between FTN-ISI and channel-ISI causes significant performance degradation in conventional detection algorithms. To address this limitation, we first derive a practical FTN transmission model over DSF channels and construct the corresponding coupled interference matrix. Considering that data detection relies on efficient channel estimation, we propose a channel estimation algorithm with joint recovery of resolvable subpath parameters. This algorithm decomposes propagation paths into resolvable subpaths with independent delay-Doppler characteristics, achieving enhanced estimation accuracy through joint gain-phase optimization. Finally, building on the derived transceiver model and coupled interference matrix, we propose a whitening-enhanced orthogonal approximate message passing (WE-OAMP) algorithm that suppresses coupled interference through iterative linear-nonlinear estimation while maintaining spectral compactness. This algorithm constructs a whitening matrix using the FTN-ISI matrix to suppress noise correlation, and then performs detection through iterative linear and nonlinear estimation. Simulation results validate that the WE-OAMP algorithm outperforms benchmark algorithms in terms of bit error rate performance, especially in coded systems. Furthermore, we derive the achievable capacity of FTN signaling with WE-OAMP detection, demonstrating capacity improvement compared to Nyquist systems.

With the rapid popularization of Internet of Things (IoT) devices, the freshness of data has become a key factor affecting decision quality and system efficiency. The application of unmanned aerial vehicle (UAV) technology provides a new solution for IoT data collection. This article mainly studies how multiple UAVs can improve the freshness of IoT data collection through joint optimization of trajectory planning and channel selection in a three-dimensional (3D) interference environment. We conducted markov decision process (MDP) modeling on the combinatorial optimization problem of the model and proposed an intelligent joint trajectory planning and channel selection for data collection (ITPCS-DC) algorithm based on multi-agent deep reinforcement learning (MADRL). This algorithm can not only avoid the agent falling into local optimum caused by 3D interference, but also effectively reduce the age of information (AoI) of IoT data collection. Simulation results show that the proposed ITPCS-DC algorithm can achieve higher rewards, lower average AoI, reduced channel switching costs, and shorter trajectory lengths compared to other benchmark algorithms. Moreover, it has better adaptability to more complex collaborative environments.

This paper advocates a fluid antenna system (FAS)-assisted long-range communication (LoRa-FAS) for Internet-of-Things (IoT) applications. In the proposed system, FAS provides spatial diversity gains for LoRa, eliminating the necessity for integrating multiple-input multiple-output (MIMO) technologies into the system. It consists of a traditional LoRa transmitter with a fixed-position antenna and a LoRa receiver employing the FAS (Rx-FAS). The pilot sequence overhead and placement for FAS are also considered. Specifically, we consider embedding pilot sequences within symbols to reduce the impact of pilot overhead on system throughput and the physical layer (PHY) frame structure, leveraging the fact that the pilot sequences do not convey source information and correlation detection at the LoRa receiver need not be performed across the entire symbol. The achievable performance of LoRa-FAS is thoroughly analyzed under both coherent and non-coherent detection schemes. We obtain new closed-form approximations for the probability density function (PDF) and cumulative distribution function (CDF) of the FAS channel under the block-correlation model. Furthermore, the approximate SER, equivalently the bit error rate (BER), of the proposed LoRa-FAS is also derived in closed form. Simulation results indicate that substantial SER gains can be achieved by FAS within the LoRa framework, even with a limited size of FAS. In addition, our analytical results align well with Clarke’s exact spatial correlation model. Finally, when utilizing the block-correlation model, we suggest that the correlation factor should be selected as the proportion of the eigenvalues of the exact correlation matrix greater than 1 for higher accuracy.

In this paper, we investigate a reconfigurable intelligent surface (RIS) aided spectrum sharing scheme between a multiple input multiple-output (MIMO) radar and MIMO multiuser communication system under non-homogeneous interference scenarios, including the interference from scattering points, the mutual interference between the two systems, and the interference among multiple users. We consider a flexibly-weighted framework for joint resource allocation, aiming at maximizing the mutual information of both the radar and the communication systems under the usual constraints on the transmit power and on the compatibility of the space spectral. To deal with the resulting triple degrees of freedom non-convex framework, a sub-optimal procedure, based on iterative alternating maximization of three suitably derived subproblems, is proposed and analyzed. Each yields a closed-form solution. In particular, to address the constant modulus constraint imposed by the RIS, we propose two different optimization strategies, based on the Minorization-Maximization framework in conjunction with the Alternating Direction Penalty Method and the Element Block Coordinate Descent formulations, namely, MM-ADPM and MM-EBCD, respectively. Finally, simulation results compare the effectiveness and advantages of the two algorithms.

Fluid antenna multiple access (FAMA) has recently emerged as a simple, promising scheme for large-scale multiuser connectivity, offering strong scalability with low implementation complexity. Nevertheless, most existing FAMA studies focus on downlink transmission under perfect channel state information (CSI) at the receiver side, while the uplink counterpart remains largely unexplored. This paper proposes a novel codebook-based port selection and combining (CPSC) FAMA framework for the uplink communications without CSI at the base station (BS). In the proposed scheme, a predefined codebook is designed and broadcast by the BS. Each user equipment (UE) employs a fluid antenna, acquires its local CSI and independently chooses the most suitable codeword, activates the corresponding fluid antenna ports, and determines the combining weights to achieve a two-way match between the selected codeword and the instantaneous effective channel. The BS then separates the superimposed user signals through codebook-guided projection operations without requiring global CSI or multiuser joint optimization. To handle potential codeword collisions, three lightweight scheduling strategies are introduced, offering flexible trade-offs between signaling overhead and collision avoidance. Simulation results demonstrate that the proposed CPSC-FAMA approach achieves substantially higher rates than fixed-antenna systems while maintaining low complexity. Moreover, the results confirm that amortizing the optimization cost over the UEs effectively reduces the BS processing burden and enhances scalability, making the proposed scheme a strong candidate for future sixth-generation (6G) networks.

The fluid antenna system (FAS) concept represents shape-flexible and position-flexible antenna technologies designed to enhance wireless communication applications. In this paper, we apply this concept to reconfigurable intelligent surfaces (RISs), introducing fluid RIS (FRIS), where each tunably reflecting element becomes afluid elementwith additional position reconfigurability. This new paradigm is referred to as fluid RIS (FRIS). We investigate an FRIS-programmable wireless channel, in which the fluid metasurface is divided into non-overlapping subareas, each acting as a fluid element that can dynamically adjust both its position and phase shift of the reflected signal. We first analyze the single-user, single-input single-output (SU-SISO) channel, in which a single-antenna transmitter communicates with a single-antenna receiver via an FRIS. The achievable rate is then maximized by optimizing the fluid elements using a particle swarm optimization (PSO)-based approach. Next, we extend our analysis to the multi-user, multiple-input single-output (MU-MISO) case, where a multi-antenna base station (BS) transmits individual data streams to multiple single-antenna users via an FRIS. In this case, the joint optimization of the positions and phase shifts of the FRIS element, as well as the BS precoding to maximize the sum-rate is studied. To solve the problem, a combination of techniques including PSO, semi-definite relaxation (SDR), and minimum mean square error (MMSE) is proposed. Numerical results demonstrate that the proposed FRIS approach significantly outperforms conventional RIS configurations in terms of achievable rate performance.

This paper presents a novel framework for enhancing physical-layer security in integrated sensing and communication (ISAC) systems by leveraging the reconfigurability of fluid antenna systems (FAS). We propose a joint precoding and port selection (JPPS) strategy that maximizes the sum secrecy rate while simultaneously ensuring reliable radar sensing. The problem is formulated using fractional programming (FP) and solved through an iterative algorithm that integrates FP transformations with successive convex approximation (SCA). To reduce computational complexity, we further develop low-complexity schemes based on zero-forcing (ZF) precoding, combined with greedy port selection and trace-inverse minimization. Simulation results show substantial improvements in both secrecy performance and sensing accuracy compared to conventional baselines, across a wide range of FAS port number, user loads, and sensing targets. These findings highlight the critical importance of FAS geometry optimization in enabling secure and efficient joint communication-sensing for next-generation wireless networks.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for wireless communication systems due to its ability to manipulate electromagnetic waves. With advantages such as low hardware complexity and low power consumption, RIS shows significant potential in positioning applications. This paper presents an RIS-based wireless signal sensing method that operates under the constraint of passive reflection while leveraging the space-time coding capabilities of RIS. By applying a space-time coding matrix on the RIS, the beamspace domain and the angle of arrival (AoA) of signals incident on the RIS can be efficiently estimated, requiring only the processing of single-channel received signals at the access point. Building upon this, a positioning prototype system utilizing two 27 GHz millimeter-wave RIS panels is developed and implemented, supporting real-time user positioning. Experimental results demonstrate that the prototype system achieves centimeter-level positioning accuracy, with errors below 10 cm in 97.22% of measurement cases, thereby validating the effectiveness of the proposed sensing and positioning scheme. These findings may pave the way for further exploration of RIS-based integration of sensing and communication technologies.

This paper studies the use of multiple planar fluid antennas at a full-duplex base station (BS) for integrated sensing and communication (ISAC). In this model, the BS communicates with a downlink user, an uplink user, and performs target sensing simultaneously. Our objective is to maximize the communication sum-rate of the up and downlink users while meeting the sensing and power constraints. Given that the problem is non-convex, we first reformulate the problem using the fractional programming (FP) framework. After that, we iteratively optimize the beamforming vectors of the BS, the uplink transmit power from the user, and the antenna positions of both transmit and receive fluid antenna systems (FASs) at the BS. In particular, the transmit and receive beamforming vectors are optimized by utilizing the majorization-minimization (MM) framework, and a closed-form solution for the uplink transmit power is derived. To optimize the BS antenna positions, we transform the problems into convex quadratically constrained quadratic programs (QCQP) by using Taylor series expansion. The subproblems can then be solved based on the successive convex approximation (SCA). Simulation results show that FAS can greatly improve the communication rate compared to the traditional fixed-position antenna (FPA) system.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems based on the concept of exploiting constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Moreover, an “indirect power scaling” method is proposed for the precoding design based on DAC quantization to enhance the ISAC performance. From the simulation results, we can observe that the proposed precoding schemes can achieve satisfactory ISAC performance with low complexity. In the considered ISAC systems, increasing the quantization bits for DAC and PS quantizations can improve the ISAC performance, and the gain for DAC quantization is more pronounced.

Based on our current understanding, extreme massive access over the same physical channel is only possible if an extra-large multiple-input multiple-output (XL-MIMO) antenna is used at the base station (BS) and instantaneous channel state information (CSI) is known at the BS side for precoding design. This casts doubt on scalability and challenges in device-to-device situations in which there is not a centralized, optimized BS for transmitting the user signals. To address this problem, we revisit the massive connectivity challenge by considering the case where no CSI is available at the BS and no precoding is used. In this situation, inter-user interference (IUI) mitigation can only be performed at the user terminal (UT) side. Leveraging the position flexibility of fluid antenna system (FAS), we adopt a fluid antenna multiple access (FAMA) approach that exploits the interference signal fluctuation in the spatial domain. Specifically, we assume that we haveNspatially correlated received signals per symbol duration from FAS. Our main approach uses a simple heuristic port shortlisting method that identifies promising ports to obtain favourable received signals that can be combined via maximum ratio combining (MRC) to form the received output signal for final detection. On top of this, a pre-trained deep joint source-channel coding (JSCC) scheme is employed, which together with a diffusion-based denoising model (MixDDPM) at the UT side, can improve the IUI immunity. We refer to the proposed scheme as turbo FAMA. Simulation results show that with a physical FAS size of 20 wavelengths at each UT transmitting quaternary phase shift keying (QPSK) symbols, fast FAMA can support 50 users whileturboFAMA can handle up to 200 users if the required symbol error rate (SER) is 10-2. If a higher error tolerance is acceptable, say SER at 0.1, turbo FAMA can even serve up to 1000 users but fast FAMA is only able to handle 160 users, all remarkably achieved without CSI at the BS.

Fluid antenna systems (FAS) are emerging as a transformative enabler for sixth-generation (6G) wireless communications, providing unprecedented spatial diversity through dynamic reconfiguration of antenna ports. However, the inherent spatial correlation among ports poses significant challenges for accurate analysis. Conventional models such as Jakes are analytically intractable, while oversimplified constant-correlation models fail to capture the true behavior. In this work, we address these challenges by applying the variable block-correlation model (VBCM) -- originally proposed by Ramírez-Espinosa \textit{et al.} in 2024 -- to FAS security analysis, and by developing comprehensive optimization methods to enhance analytical accuracy. We derive new closed-form expressions for average secrecy capacity (ASC) and secrecy outage probability (SOP), demonstrating that the VBCM framework achieves simulation-aligned accuracy, with relative errors consistently below $5\%$ (compared to $10$--$15\%$ for constant-correlation models). To maximize ASC, we further design two algorithms: a grid search (GS) method and a gradient descent (GD) method. Numerical results reveal that the VBCM-based approach not only provides reliable insights into FAS security performance, but also yields substantial gains -- ASC improvements exceeding $120\%$ in high-threat scenarios and $18$--$19\%$ performance enhancements for compact antenna configurations. These findings underscore the practical value of integrating VBCM into FAS security analysis and optimization, establishing it as a powerful tool for advancing 6G communication systems.

In response to the “security blind zone” challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user’s achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.

This paper proposes a novel pattern-reconfigurable fluid reconfigurable intelligent surface (FRIS) framework, where each fluid element can dynamically adjust its radiation pattern based on instantaneous channel conditions. To evaluate its potential, we first conduct a comparative analysis of the received signal power in point-to-point communication systems assisted by three types of surfaces: (1) the proposed pattern-reconfigurable FRIS, (2) a position-reconfigurable FRIS, and (3) a conventional RIS. Theoretical results demonstrate that the pattern-reconfigurable FRIS provides a significant advantage in modulating transmission signals compared to the other two configurations. To further study its capabilities, we extend the framework to a multiuser communication scenario. In this context, the spherical harmonics orthogonal decomposition (SHOD) method is employed to accurately model the radiation patterns of individual fluid elements, making the pattern design process more tractable. An optimization problem is then formulated with the objective of maximizing the weighted sum rate among users by jointly designing the active beamforming vectors and the spherical harmonics coefficients, subject to both transmit power and pattern energy constraints. To tackle the resulting non-convex optimization problem, we propose an iterative algorithm that alternates between a minimum mean-square error (MMSE) approach for active beamforming and a Riemannian conjugate gradient (RCG) method for updating the spherical harmonics coefficients. Simulation results show that the proposed pattern-reconfigurable FRIS significantly outperforms traditional RIS architectures based on the 3GPP 38.901 and isotropic radiation models, achieving average performance gains of 161.5% and 176.2%, respectively. Additionally, it reduces the required number of antennas and RIS elements by over 300%, offering substantial improvements in hardware efficiency.

This paper proposes a hardware-software co-design approach to efficiently optimize beamforming and port selection in fluid antenna systems (FASs). To begin with, a fluid-antenna (FA)-enabled downlink multi-cell multiple-input multiple-output (MIMO) network is modeled, and a weighted sum-rate (WSR) maximization problem is formulated. Second, a method that integrates graph neural networks (GNNs) with random port selection (RPS) is proposed to jointly optimize beamforming and port selection, while also assessing the benefits and limitations of random selection. Third, an instruction-driven deep learning accelerator based on a field-programmable gate array (FPGA) is developed to minimize inference latency. To further enhance efficiency, a scheduling algorithm is introduced to reduce redundant computations and minimize the idle time of computing cores. Simulation results demonstrate that the proposed GNN-RPS approach achieves competitive communication performance. Furthermore, experimental evaluations indicate that the FPGA-based accelerator maintains low latency while simultaneously executing beamforming inference for multiple port selections.

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

In a single-antenna fluid antenna system (FAS), the transceiver dynamically selects the antenna port with the strongest instantaneous channel to enhance link reliability. However, deriving accurate yet tractable performance expressions under the fully correlated fading channel remains challenging, primarily due to the absence of a closed-form distribution for the FAS channel. To address this gap, this paper develops a novel performance evaluation framework for FAS under fully correlated Rayleigh fading by modeling the FAS channel using extreme value distributions (EVDs). We first justify the suitability of EVDs and model the FAS channel as a Gumbel distribution, whose parameters, estimated via the maximum likelihood (ML) criterion, are expressed as functions of the number of ports and antenna aperture size. Closed-form approximate expressions for the outage probability (OP) and the ergodic capacity (EC) are then derived. Simulation results show that the Gumbel distribution provides simple yet sufficiently accurate expressions for EC, although slight deviations in OP accur in the low-probability region of practical interest. To further improve accuracy, the FAS channel is modeled using the generalized extreme value (GEV) distribution, yielding closed-form expressions for OP and EC based on ML-estimated parameters. Simulation results confirm that the GEV distribution provides superior accuracy compared to the Gumbel, particularly for OP, while both EVD-based approaches offer computationally efficient and analytically tractable tools for evaluating FAS performance under the fully correlated fading channel.

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems.

Hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks (SUTNs) can provide maritime users with ubiquitous communication services. However, eavesdropping poses a significant challenge to the secure communications of SUTNs due to their wide-area coverage. In this paper, we propose a novel secure scheme for maritime communications, where a terrestrial-UAV integrated network coexists with marine satellite (MS) systems in the presence of an eavesdropper (Eve). Considering imperfect channel state information (CSI) for both the MS and Eve, we focus on the collaborative design of beamforming for the terrestrial base station (TBS), UAV, and MS, as well as the UAV’s trajectory. A robust optimization problem is formulated to maximize the worst-case secrecy rate, subject to constraints on worst-case communication quality for each user, UAV locations, and TBS backhaul throughput. To tackle this intractable non-convex problem, we leverage the S-procedure, general sign-definiteness, and successive convex approximation (SCA) to propose a security solution that efficiently optimizes all variables using convex optimization techniques. Numerical results validate the effectiveness of the proposed solution, illustrating the impact of CSI errors and the secure performance enhancements achieved through joint trajectory and beamforming optimization.

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a promising technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen as a result of the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the SAR-aware multiuser multiple-input multiple-output (MIMO) communications assisted by FAS. In particular, a two-layer iterative algorithm is proposed to minimize the SAR value under signal-to-interference-plus-noise ratio (SINR) and FAS constraints. Moreover, the minimum weighted SINR maximization problem under SAR and FAS constraints is studied by finding its relationship with the SAR minimization problem. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

In this paper, we introduce a novel framework for multiple access code design under the finite blocklength regime in multi-input and multi-output (MIMO) systems, termed coded pattern multiple access (CPMA). CPMA involves a series of multiple access code designs facilitated by a sparse pattern mapper/demapper, enabling independent information projection onto transmission patterns. Unlike existing approaches, the mapping and demapping of patterns are completely isolated components, ensuring energy-efficient transmission. In this work, we establish and analyze practical CPMA models, thoroughly investigating the performance limits of a potential non-bijective demapper. Closed-form and integral-form solutions are provided to describe these performance limits. Additionally, we present a practical application of CPMA: the coded pattern unsourced random access (CPURA) scheme. This scheme is designed for finite blocklength transmission under a quasi-static fading channel. The proposed CPURA achieves bound-approaching performance for large user groups, outperforming existing state-of-the-art methods in the context of massive machine-type communications (mMTC). Notably, the minimum required energy-per-bit to support 1,200 active users exhibits only a 1.3 dB gap from the achievable bound, validating the potential of the proposed CPMA framework.

Fluid antenna system (FAS) revolutionizes wireless communications via utilizing position-flexible antennas that dynamically optimize channel conditions and mitigate multipath fading. This innovation is particularly valuable in indoor environments, in which signal propagation is severely degraded due to structural obstructions and complex multipath reflections. In this paper, we investigate the channel modeling and the joint optimization of antenna positioning, beamforming, and power allocation for indoor FAS. In particular, we propose a layout-specific channel model, and employ the novel group relative policy optimization (GRPO) algorithm for tackling the optimization problem. Compared to the state-of-the-art Sionna model, our model achieves an 83.3% reduction in computation time with an approximately 3 dB increase in root-mean-square error (RMSE). When simplified to a two-ray model, our model allows for a closed-form antenna position solution with near-optimal performance. For the joint optimization problem, our GRPO algorithm outperforms proximal policy optimization (PPO) and other baselines in sum-rate, while requiring only 50.8% computational resources of PPO, thanks to its group advantage estimation. Simulation results show that increasing either the group size or trajectory length in GRPO does not yield significant improvements in sum-rate, suggesting that these parameters can be selected conservatively without sacrificing performance.

This paper investigates fluid antenna systems (FASs) subject to finite-blocklength (FBL) constraints, motivated by the strict reliability-latency and ultra-massive connectivity requirements of future wireless networks. While FAS performance has been widely studied in the asymptotic regime, its behavior under FBL remains largely unexplored. Our objective is to develop a unified set of analytical tools for evaluating FASs under FBL that remains applicable across different spatial-correlation models. First, to establish accurate benchmarks for non-orthogonal finite-length user signature design, we characterize both the average and the worst-case correlation coefficients via extreme value theory (EVT) and derive closed-form predictions of the achievable correlation levels. Second, taking block error rate (BLER) as the fundamental FBL metric, we study joint detection and decoding in FAS-assisted links and derive a closed-form BLER expression that is universally applicable across channel models. Additionally, we revisit outage probability (OP) in the FBL regime and obtain tractable OP characterizations for both FASs and conventional multiple fixed-position antenna (FPA) systems. In order to reduce the computational burden for multi-fold integrals in correlated fading models, we further propose a Taylor-expansion-assisted mean value theorem for integrals (MVTI), thus enabling efficient performance evaluation with marginal accuracy loss. Numerical results validate the analysis and reveal that even single-antenna FASs can have superior spatial diversity relative to conventional multi-FPA systems. Moreover, under both FBL and interference-limited environments, FASs provide improved energy, spectral, and hardware efficiencies, hence highlighting FAS as a promising enabler for next-generation wireless networks.

Suffering from serious rate degradation, how to improve transmission rate with low latency is a challenging issue in ultra-reliable and low-latency communications (URLLC), especially when there is no enough blocklength for data transmission. To handle this issue, we propose to integrate the reconfigurable intelligent surface (RIS) and superimposed pilot (SP) into massive multiple-input multiple-output (mMIMO) systems, where the SP ensures latency by simultaneously sending pilot and data while the RIS improves high transmission rate by reflecting the SP signals. Practically, we derive the finite blocklength ergodic achievable rate lower bound in closed form under imperfect channel estimation and pilot interference removal. Then, we maximize the weighted sum rate of all the users by jointly designing the power control of SP at each user and the phase shifts at the RIS. Due to the highly coupled variables, we first decompose the original problem into the phase shift design subproblem and the power control design subproblem, which are resolved by a genetic algorithm (GA) and an iterative algorithm based on geometric programming (GP). Then, a block coordinate descent algorithm is proposed. Correspondingly, the complexity and convergence of the proposed algorithms are analyzed. Finally, our numerical results demonstrate that the joint design scheme can bring effective rate improvement in stringent latency constraints.

Utilizing communication signals to extract motion parameters has emerged as a key direction in Vehicle-to-Everything (V2X) networks. Accurately modeling the relationship between communication signals and sensing performance is critical for the advancement of such systems. Unlike prior work that relies primarily on qualitative analysis, this paper derives the Cramér-Rao Bound (CRB) for radar parameter estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) waveforms and Uniform Planar Array (UPA) configurations. Recognizing that vehicles may act as extended targets, we propose two New Radio (NR)-V2X-compatible beamforming schemes tailored to different phases of the communication process. During the initial beam establishment phase, we develop a beamforming approach based on the union of predictive error ellipses, which enhances scatterer localization through temporally assisted beam training. In the beam adjustment phase, we introduce an adaptive narrowest-beam strategy that leverages the positions of scatterers and the communication receiver (CR), enabling effective tracking with reduced complexity. The beam design problem is addressed using the minimum enclosing ellipse algorithm and tailored antenna control methods. Simulation results validate the proposed approach, showing up to a 32.4% improvement in achievable rate with a 32×32 transmit antenna array and a 5.2% gain with an 8×8 array, compared to conventional beam sweeping under identical SNR conditions.

This paper develops a comprehensive framework for the performance analysis of fluid antenna system (FAS)-enabled unmanned aerial vehicle (UAV) relaying networks operating in the finite blocklength regime. This work establishes a rigorous methodology for characterizing system reliability under diverse propagation environments. Closed-form expressions for the block error rate (BLER) are derived by employing a tractable eigenvalue-based approximation of the spatially correlated UAV-to-user link, whose underlying independent diversity components are modeled as Nakagami-mfading. This approach addresses both line-of-sight (LoS) dominant rural and probabilistic non-line-of-sight (NLoS) urban scenarios. Furthermore, a high signal-to-noise ratio (SNR) asymptotic analysis is developed, revealing the fundamental diversity order of the UAV-to-user link. Based on this, we further address the practical issue of energy efficiency. A realistic energy efficiency maximization problem is formulated, which explicitly accounts for the time and energy overhead in the FAS port selection process. An efficient hierarchical algorithm is then proposed to jointly optimize the key system parameters. Extensive numerical results validate the analysis and illustrate that while FASs can yield substantial power gains, the operational overhead introduces a non-trivial trade-off, leading to an optimal number of ports and fundamentally different UAV deployment strategies in rural versus urban environments. In summary, this work provides both foundational analysis and practical design guidelines for FAS-enabled UAV communications.

Fluid antenna systems (FAS) utilize position reconfigurability to improve spatial diversity in wireless communications. However, a rigorous framework for error probability analysis under spatially correlated channels remains absent. This paper fills this gap by deriving a closed-form asymptotic expression for the symbol error rate (SER). This mathematical expression establishes the fundamental scaling law between the error performance and the spatial correlation matrix. A key insight from our analysis is that the achievable diversity gain depends entirely on the effective rank of the spatial channel, rather than the total number of antenna ports. To quantify this effective rank, we propose a dual approach: a theoretical derivation and a geometry-based algorithm. Both methods rigorously prove that the effective rank converges to a fundamental limit of$2W+1$, where$W$denotes the normalized aperture width. Specifically, the geometry-based algorithm extracts distinct performance thresholds from the eigenvalue spectrum of the channel. These thresholds perfectly match the derived theoretical limit. Furthermore, the proposed effective rank model demonstrates higher accuracy than existing approaches in the literature. Based on this robust framework, we offer a complete characterization of diversity gains and coding gains. The analytical results reveal a definitive design principle: enlarging the physical aperture increases the effective rank and drives performance improvements, whereas simply increasing port density within a fixed aperture yields diminishing returns.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems with the assistance of constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes based on them. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Simulation results have been implemented to validate the superiority of the proposed algorithms. When the number of elements in the quantization sets is fixed, the ISAC performance of the precoding scheme based on DAC quantization is superior to that of the precoding scheme based on PS quantization, thanks to the more dispersed level distribution of DAC quantization.

This paper investigates the energy efficiency (EE) of the fluid antenna relay (FAR)-assisted wireless communication systems in non-line-of-sight (NLoS) scenarios. Unlike conventional fixed-position antenna systems, the FAR dynamically adjusts the spatial positions of fluid antennas (FAs), enabling efficient signal transmission through blockages. By integrating the amplify-and-forward (AF) protocol, the proposed FAR architecture amplifies and forwards signals while controlling phase shifts via FA reconfiguration. An optimization problem is formulated to maximize the system EE under given constraints. The problem is decomposed into three sub-problems including large-scale fading optimization, small-scale fading optimization, and joint power control and beamforming design optimization. These subproblems are solved iteratively with successive convex approximation (SCA) and Dinkelbach methods. Numerical simulation results demonstrate that the proposed algorithm significantly outperforms the existing STAR-RIS and AF relay schemes, improving EE of the system by up to 29.92% and 45.04%, respectively. The work in this paper bridges the research gap in FAS research with NLoS challenges and provides a framework for future FAR-enabled wireless communication systems.

This paper investigates joint transmit beamforming and trajectory optimization techniques for an unmanned aerial vehicle (UAV)-enabled covert full-duplex (FD) integrated sensing and communication (ISAC) systems with hardware impairments (HWIs), where the aerial access point (AP) transmits and receives sensing signals while the integrated communication operates in either downlink or uplink. We jointly optimize the downlink transmit signal and the uplink receive beamformers at the AP, the transmit power at the uplink users and the trajectory of the UAV. An optimization problem is formulated for maximizing the minimum covert transmission rate (CTR) among all covert users (CUs) subject to the constraints of the required sensing and uplink transmission capabilities, system covertness, total power budget. To tackle the intractable non-convex problem, we leverage majorization-minimization (MM) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Numerical results demonstrate the effectiveness of the proposed method in balancing the trade-off between covert communication and sensing performance, highlighting the UAV’s potential in adaptive ISAC deployment.

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.

In this paper, we investigate a joint communication and computation resource allocation strategy for an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system employing fluid antenna (FA). Specifically, each user is equipped with an FA to offload the entire computation tasks to the MEC server deployed on the UAV. By dynamically selecting antenna ports, users can achieve latency-efficient edge computing services, especially advantageous in dynamic environments. To minimize the maximum execution delay of all the users, we jointly optimize the UAV location, FA port selection, and computation resource allocation, subject to computational capacity constraints. The original non-convex optimization problem is decomposed into three tractable subproblems within a block coordinate descent (BCD) algorithm. The optimal computing frequencies are derived in closed form, while the UAV location and FA port selection are optimized using low-complexity iterative algorithms based on successive convex approximation (SCA) and linear programming (LP) techniques. In addition to conventional benchmarks with fixed-position antennas (FPAs), we also introduce a reconfigurable intelligent surface (RIS)-assisted system as a comparative baseline. Simulation results demonstrate that the proposed FA-assisted scheme significantly outperforms both FPAs and RIS-assisted counterparts, with performance gains becoming more pronounced in multi-task and highly dynamic scenarios, establishing FA-assisted UAV-MEC as a promising solution for future deployments.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. A FAS possesses position reconfigurability to switch on-demand among$N$predefined ports over a prescribed space. This paper explores the performance of a singleinput single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closedform bounds for the outage probability of the Rx-SISO-FAS model with uniform linear array port configuration. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,$N$, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:$i$) the Rx-SISO-FAS model can enhance performance under spatiallycorrelated Rician fading channels over the fixed-position antenna counterpart;$i i)$the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime.

Integrated sensing and communication (ISAC) is seen as a future solution to frequency congestion due to its excellent ability to simultaneously support target sensing and information transmission. However, it also introduces a potential security threat due to the sensing behavior. In this paper, we propose a covert ISAC scheme against collusive wardens. In particular, a dual-function base station continuously sense an aerial target while communicating with a ground receiver. First, we derive a closed-form expression of each warden's detection outage probability to obtain the global detection outage probability. Then, we jointly optimize the communication and sensing beamformings to maximize the covert transmission rate under the worst case that all wardens can collusively adjust their detection thresholds to achieve the best detection. To tackle this non-convex optimization problem, an iteration scheme is proposed. Numerical results demonstrate the validity of the proposed covert ISAC scheme.

This paper studies the ergodic achievable rates of a primitive Gaussian diamond channel with Rayleigh fading. The system is modeled as a two-hop relay channel where a single user communicates with a central processor (CP) through two relays. These relays are agnostic to the user's codebooks and are considered “primitive” because the fronthaul links are error-free but have limited capacity. In this setup, the channel state information (CSI) is assumed to be available only at the relays and not at the CP. Despite the simplicity of this configuration, deriving an accurate characterization of the ergodic capacity is surprisingly challenging. To address this, we first establish an analytical rate upper bound, assuming that the relays can cooperate and that the CP has access to the CSI as well. In order to obtain lower bounds, we resort to specific analytically/numerically tractable achievability strategies. When designing such strategies, we need to take into account that the CP has no CSI and that each relay has only statistical knowledge of the CSI other relay. Under these constraints, we propose two achievable schemes employing different estimation and compression methods at relays. Simulation results show that these schemes achieve performance close to the derived upper bound over a wide range of system parameters.

In this work, we discuss the problem of unsourced random access (URA) over a Gaussian multiple access channel (GMAC). To address the challenges posed by emerging massive machine-type connectivity, URA reframes multiple access as a coding-theoretic problem. The sparse code-oriented schemes are highly valued because they are widely used in existing protocols, making their implementation require only minimal changes to current networks. However, drawbacks such as the heavy reliance on extrinsic feedback from powerful channel codes and the lack of transmission robustness pose obstacles to the development of sparse codes. To address these drawbacks, a novel sparse code structure based on a universally applicable power division strategy is proposed. Comprehensive numerical results validate the effectiveness of the proposed scheme. Specifically, by employing the proposed power division method, which is derived analytically and does not require extensive simulations, a performance improvement of approximately 2.8 dB is achieved compared to schemes with identical channel code setups.

This paper addresses the unsourced/uncoordinated random access problem in an integrated sensing and communications (ISAC) system, with a focus on uplink multiple access code design. Recent theoretical advancements highlight that an ISAC system will be overwhelmed by the increasing number of active devices, driven by the growth of massive machine-type communication (mMTC). To meet the demands of future mMTC network, fundamental solutions are required that ensure robust capacity while maintaining favorable energy and spectral efficiency. One promising approach to support emerging massive connectivity is the development of systems based on the unsourced ISAC (UNISAC) framework. This paper proposes a spectrum-sharing compressive sensing-based UNISAC (SSCS-UNISAC) and offers insights into the practical design of UNISAC multiple access codes. In this framework, both communication signals (data transmission) and sensing signals (e.g., radar echoes) overlap within finite channel uses and are transmitted via the proposed UNISAC protocol. The proposed decoder exhibits robust performance, providing 20-30 dB capacity gains compared to conventional protocols such as TDMA and ALOHA. Numerical results validate the promising performance of the proposed scheme.

This paper examines the impact of emerging fluid antenna systems (FAS) on reconfigurable intelligent surface (RIS)-aided secure communications. Specifically, we consider a classic wiretap channel, where a fixed-antenna transmitter sends confidential information to an FAS-equipped legitimate user with the help of an RIS, while an FAS-equipped eavesdropper attempts to decode the message. To evaluate the proposed wireless scenario, we first introduce the cumulative distribution function (CDF) and probability density function (PDF) of the signal-to-noise ratio (SNR) at each node, using the central limit theorem and the Gaussian copula function. We then derive a compact analytical expression for the secrecy outage probability (SOP). Our numerical results reveal how the incorporation of FAS and RIS can significantly enhance the performance of secure communications.

The rapid evolution of communication technologies and the emergence of sixth-generation (6G) networks have introduced unprecedented opportunities for ultra-reliable, low-latency, and energy-efficient communication. Integrating technologies like non-orthogonal multiple access (NOMA) and wireless powered communication networks (WPCNs) brings new challenges. These include energy constraints and increased security vulnerabilities. Traditional antenna systems and orthogonal multiple access schemes struggle to meet the increasing demands for performance and security in such environments. To address this gap, this paper investigates the impact of emerging fluid antenna systems (FAS) on the performance of physical layer security (PLS) in WPCNs. Specifically, we consider a scenario in which a transmitter, powered by a power beacon via an energy link, transmits confidential messages to legitimate FAS-aided users over information links while an external eavesdropper attempts to decode the transmitted signals. Additionally, users leverage the NOMA scheme, where the far user may also act as an internal eavesdropper. For the proposed model, we first derive the distributions of the equivalent channels at each node and subsequently obtain compact expressions for the secrecy outage probability (SOP) and average secrecy capacity (ASC), using the Gaussian quadrature methods. Our results reveal that incorporating the FAS for NOMA users, instead of the TAS, enhances the performance of the proposed secure WPCN.

Cell-free multiple-input-multiple-output generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity.

This article established a novel multi-input multioutput (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter, the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two subproblems. For the beamforming matrices optimization of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

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

Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FASs) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study an FAS-driven CR setup where a secondary user (SU) adjusts the positions of fluid antennas to detect signals from the primary user (PU). We aim to maximize the detection probability under the constraints of the false alarm probability and the received beamforming of the SU. To address this problem, we first derive a closed-form expression for the optimal detection threshold and reformulate the problem to find its solution. Then, an alternating optimization (AO) scheme is proposed to decompose the problem into several subproblems, addressing both the received beamforming and the antenna positions at the SU. The beamforming subproblem is addressed using a closed-form solution, while the fluid antenna positions are solved by successive convex approximation (SCA). Simulation results reveal that the proposed algorithm provides significant improvements over traditional fixed-position antenna (FPA) schemes in terms of spectrum sensing performance.

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas’ positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is nonconvex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas’ positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes.

Surface wave communication (SWC) is an emerging technology garnering significant interest for its diverse potential applications in communications. However, accurately computing electromagnetic field strength, which is related to the path loss, in reconfigurable surface structures, particularly for long-distance transmission, presents an ongoing challenge. To address this, we introduce a novel analytical model employing surface wave ray tracing. Unlike conventional simulations, our analytical approach enables precise computation of the electromagnetic field strength attenuation in both short and long-distance transmissions, providing invaluable insights for practical SWC implementations. Our proposed model takes into account key system parameters such as surface material, thickness, cavity porosity, and other variables influencing propagation performance. This facilitates analysis of optimal reconfigurable structures. Simulation results validate the model’s accuracy in short-distance transmission, thereby endorsing its effectiveness in studying surface wave path loss over longer distances. Furthermore, our study demonstrates the SWC superiority over traditional coaxial cable and space-wave communication in mitigating path loss. Additionally, we explore the impacts of various factors such as different dielectric layers, wall materials, leakage, and pathway width on SWC performance, providing deeper insights into designing optimal reconfigurable structures for SWC applications.

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

Fluid antenna system (FAS) is an emerging technology that uses the new form of shape- and position-reconfigurable antennas to empower the physical layer for wireless communications. Prior studies on FAS were however limited to narrowband channels. Motivated by this, this paper addresses the integration of FAS in the fifth generation (5G) orthogonal frequency division multiplexing (OFDM) framework to address the challenges posed by wideband communications. We propose the framework of the wideband FAS-OFDM system that includes a novel port selection matrix. Then we derive the achievable rate expression and design the adaptive modulation and coding (AMC) scheme based on the rate. Extensive link-level simulation results demonstrate striking improvements of FAS in the wideband channels, underscoring the potential of FAS in future wireless communications.

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.

This paper addresses the problem of pilot contamination in single-cell networks, especially in dense-user areas where intra-cell pilot reuse is inevitable. We utilize fluid antennas (FAs) at the base station (BS) to improve the uplink (UL) channel estimation accuracy in terms of normalized mean square error (NMSE). To this end, we invoke channel spatial correlation as a key metric to characterize the similarity among the spatial structures of users’ channels. Inspired by the fact that the users with low spatial correlation experience less interference, we propose to mitigate pilot contamination by optimizing the FA positions with an innovative objective of minimizing the channel spatial correlation among the pilot-sharing users. To handle the resulting fractional programming problem, we recast it into a more tractable form via establishing upper and lower bounds for the objective function. Subsequently, the reformulated problem is effectively solved through an alternating optimization (AO) framework, where the position of each FA is iteratively optimized exploiting the successive convex approximation (SCA) method. Finally, simulation results validate the effectiveness of the proposed algorithm and demonstrate its remarkable performance in channel estimation and data transmission. In particular, the proposed 16-FA scheme achieves up to 71.2% NMSE reduction over its fixed-position antenna (FPA) counterpart, translating to an approximate 11 dB SNR gain in symbol detection.

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

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive Cram´er-Rao bound (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks.

This paper presents a general framework for probabilistic relay selection (RS) in asymmetric buffer-aided cooperative relaying systems, which caters to scenarios with both perfect and imperfect channel state information (CSI) during the RS process. The framework extends and generalizes many existing buffer-aided RS schemes. In particular, we introduce an auxiliary stochastic process which assigns varying selection probabilities to different links, considering the dynamic wireless channel and buffer states. Subsequently, we leverage the obtained outage probability and average packet delay (APD) to formulate outage optimization problems while adhering to APD. To address the intricate high-dimensional optimization problems, we employ a deep learning (DL) approach, which involves designing probability mass functions for the auxiliary stochastic process and developing an effective loss function to update the neural network. Simulation results unequivocally demonstrate the superior performance of the proposed DL-based probabilistic RS scheme compared to benchmark schemes, particularly in scenarios involving imperfect CSI.

This paper investigates a multiuser millimeter-wave (mmWave) uplink system in which each user is equipped with a multi-antenna fluid antenna system (FAS) while the base station (BS) has multiple fixed-position antennas. Our primary objective is to maximize the system capacity by optimizing the transmit covariance matrices and the antenna position vectors of the users jointly. To gain insights, we start by deriving upper bounds and approximations for the capacity. Then we delve into the capacity maximization problem. Beginning with the simple scenario of a single user equipped with a single-antenna FAS, we demonstrate that a closed-form optimal solution exists when there are only two propagation paths between the user and the BS. In the case where multiple propagation paths are present, a near-optimal solution can also be obtained through a one-dimensional search method. Expanding our focus to multiuser cases, in which users are equipped with either single- or multi-antenna FAS, we show that the original capacity maximization problems can be reformulated into distinct rank-one programmings. Then, we propose alternating optimization algorithms to deal with the transformed problems. Simulation results indicate that FAS can improve the capacity of the multiple access channel (MAC) greatly, and the proposed algorithms outperform all the benchmarks.

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known channel state information (CSI) for the eavesdropping target, we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the discrete reflection coefficients (DRC) of the multiple hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

In wireless communications, the communication channel between the transmitter and receiver can be monitored by an eavesdropper. The eavesdropper uses deep learning (DL) to quickly identify the modulation parameters of signals and further disrupt legitimate communications. Since DL has been proven to be vulnerable to adversarial attacks, this paper proposes to attack the eavesdropper’s model by designing adversarial waveforms, preventing the eavesdropper from correctly identifying the modulation schemes used by legitimate users, and thereby preventing the eavesdropper from interfering with normal communications. This paper proposes an attention-based black-box attack method, which uses the prediction of different networks in the ensemble model to assign adversarial attention factors to each network. This greatly improves the transmission attack performance of the designed adversarial examples. In addition, by analysing the influence of the channel on the adversarial waveform, we further design the adversarial waveform that can be transmitted in the channel to improve the practicability of the attack algorithm. Finally, we theoretically derive the bounds of the adversarial risk increase that the attack brings to the target model. Simulation results show that the proposed method can improve the success rate of the attack on the eavesdropper’s modulation detection model, cause the model to misidentify the signal modulation type, and improve the security and reliability of legitimate transceivers in wireless communication systems.

Distributed reconfigurable intelligent surfaces (RISs) provide rich macro-diversity coverage due to different locations of the RISs, which is beneficial to combat coverage holes. However, the system performance relies on the effective coordination of multiple RISs. In particular, distributed RIS-assisted power allocation and the phase shifts of RISs should be jointly designed under nonlinear scheduling constraints. Thus, the resource allocation scheme for distributed RIS-assisted multiuser system is a crucial challenge. To tackle these issues, joint power allocation, phase shifts and communication scheduling design for distributed RIS-assisted systems is investigated in this paper, where all RISs simultaneously and cooperatively serve multiple users. To overcome the formulated nonconvex optimization problem, the original problem is decoupled into three subproblems and solved in an iterative manner. Specifically, we first consider the subproblem of power allocation, which can be solved via maximizing the ergodic achievable rate. By applying the ergodic rate, an approximate closed-form solution is formed for the power allocation. Subsequently, the phase shifts are optimized using the minimization-maximization optimization methods. Finally, a communication scheduling scheme is presented to address the scheduling variables. Numerical simulations are conducted to demonstrate that the considered solution outperforms the existing benchmark and achieves a near-optimal spectral efficiency.

Fluid antenna system (FAS) is gaining attention as an innovative technology for boosting diversity and multiplexing gains. As a key innovation, it presents the possibility to overcome interference by position reconfigurability on one radio frequency (RF) chain, giving rise to the concept of fluid antenna multiple access (FAMA). While FAMA is originally designed to deal with interference mainly by position change and treat interference as noise, this is not rate optimal, especially when suffering from a strong interference channel (IC) where all positions have strong interference. To tackle this, this paper considers a two-user strong IC where FAMA is used in conjunction with simultaneous non-unique decoding (SND). Specifically, we analyze the key statistics for the signal-to-noise ratio (SNR) and interference-to-noise ratio (INR) for a canonical two-user IC setup, and subsequently derive the delay outage rate (DOR), outage probability (OP) and ergodic capacity (EC) of the FAMA-IC. Our numerical results illustrate huge benefits of FAMA with SND over traditional fixed-position antenna systems (TAS) with SND in the fading IC.

Fluid antenna enables position reconfigurability that gives transceiver access to a high-resolution spatial signal and the ability to avoid interference through the ups and downs of fading channels. Previous studies investigated this fluid antenna multiple access (FAMA) approach in a single-cell setup only. In this paper, we consider a cell-free network architecture in which users are associated with the nearest base stations (BSs) and all users share the same physical channel. Each BS has multiple fixed antennas that employ maximum ratio transmission (MRT) to beam to its associated users while each user relies on its fluid antenna system (FAS) on one radio frequency (RF) chain to overcome the inter-user interference. Our aim is to analyze the outage probability performance of such cell-free FAMA network when both large-and small-scale fading effects are considered. To do so, we derive the distribution of the received magnitude for a typical user and then the interference distribution under both fast and slow port switching techniques. The outage probability is finally obtained in integral form in each case. Numerical results demonstrate that in an interference-limited situation, although fast port switching is typically understood as the superior method for FAMA, slow port switching emerges as a more effective solution when there is a large antenna array at the BS. Moreover, it is revealed that FAS at each user can serve to greatly reduce the burden of BS in terms of both antenna costs and CSI estimation overhead, thereby enhancing the scalability of cell-free networks.

Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system’s achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small.

Nonlinear self-interference (SI) cancellation is essential for mitigating the impact of transmitter-side nonlinearity on overall SI cancellation performance in flexible duplex systems, including in-band full-duplex (IBFD) and sub-band full-duplex (SBFD). Digital SI cancellation (SIC) must address the nonlinearity in the power amplifier (PA) and the in-phase/quadrature-phase (IQ) imbalance from up/down converters at the base station (BS), in addition to analog SIC. In environments with rich signal reflection paths, however, the required number of delayed taps for time-domain nonlinear SI cancellation increases exponentially with the number of multipaths, leading to excessive complexity. This paper introduces a novel, low-complexity, frequency domain nonlinear SIC, suitable for flexible duplex systems with multiple-input and multiple-output (MIMO) configurations. The key approach involves decomposing nonlinear SI into a nonlinear basis and categorizing them based on their effectiveness across any flexible duplex setting. The proposed algorithm is founded on our analytical results of intermodulation distortion (IMD) in the frequency domain and utilizes a specialized pilot sequence. This algorithm is directly applicable to orthogonal frequency division multiplexing (OFDM) multi-carrier systems and offers lower complexity than conventional digital SIC methods. Additionally, we assess the impact of the proposed SIC on flexible duplex systems through system-level simulation (SLS) using 3D ray-tracing and proof-of-concept (PoC) measurement.

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.

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

Fluid antenna multiple access (FAMA) exploits spatial opportunities in wireless channels through port switching to overcome multiuser interference, achieving better performance than traditional fixed MIMO systems. Integrated Data and Energy Transfer (IDET) is capable of providing both wireless data transfer (WDT) and wireless energy transfer (WET) services for low-power devices. This paper investigates an FAMA-assisted IDET system, where a base station (BS) equipped withNfixed antennas provides dedicated IDET services toNuser equipments (UEs). Each UE is equipped with a single fluid antenna, while the power splitting (PS) approach is conceived for coordinating WDT and WET. Under the Rayleigh channel model, we derive both exact expressions and approximate closed forms for the outage probabilities of WDT and WET, where the fluid antenna (FA) at each UE selects the optimal port to maximize either the signal-to-interference-plus-noise ratio (SINR) or the energy harvesting power (EHP). The IDET outage probabilities are defined and subsequently derived and approximated into closed-forms. Further, multiplexing gains of the proposed system are defined and analyzed to evaluate the performance. Further, we analyze the IDET outage probabilities and multiplexing gains of the proposed system. Additionally, to provide a more general analysis, we extend our analytical framework to the Rician channel model. Numerical results validate the theoretical analysis while also illustrating that the trade-off is achieved between WDT and WET performance by exploiting different port selection strategies and numbers of UEs.

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in autonomous aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV’s flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Using our generalized model, the optimized trajectories achieve up to 11.8% higher energy efficiency compared to those derived from conventional Gaussian pointing error models.

Fluid antenna system (FAS) has recently surfaced as a promising technology for the upcoming sixth generation (6G) wireless networks. Unlike traditional antenna system (TAS) with fixed antenna location, FAS introduces a flexible component in which the radiating element can switch its position within a predefined space. This capability allows FAS to achieve additional diversity and multiplexing gains. Nevertheless, to fully reap the benefits of FAS, obtaining channel state information (CSI) over the predefined space is crucial. In this paper, we study the system with a transmitter equipped with a traditional fixed antenna and a receiver with a fluid antenna by considering an electromagnetic-compliant channel model. We address the challenges of channel estimation and reconstruction using Nyquist sampling and maximum likelihood estimation (MLE) methods. Our analysis reveals a fundamental tradeoff between the accuracy of the reconstructed channel and the number of estimated channels, indicating that half-wavelength sampling is insufficient for perfect reconstruction and that oversampling is essential to enhance accuracy. Despite its advantages, oversampling can introduce practical challenges. Consequently, we propose a suboptimal sampling distance that facilitates efficient channel reconstruction. In addition, we employ the MLE method to bound the channel estimation error by$\epsilon $, with a specific confidence interval (CI). Our findings enable us to determine the minimum number of estimated channels and the total number of pilot symbols required for efficient channel reconstruction in a given space. Lastly, we investigate the rate performance of FAS and TAS and demonstrate that FAS with imperfect CSI can outperform TAS with perfect CSI. In contrast to existing works, we also show that there is an optimal fluid antenna size that maximizes the achievable rate when considering the energy and bandwidth overheads for full CSI acquisition.

Different from conventional reconfigurable intelligent surfaces (RIS), a recent innovation called simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has emerged, aimed at achieving complete 360-degree coverage in communication networks. Additionally, full-duplex (FD) technology is recognized as a potent approach for enhancing spectral efficiency by enabling simultaneous transmission and reception within the same time and frequency resources. In this study, we investigate the performance of a STAR-RIS-assisted FD communication system. The STAR-RIS is strategically placed at the cell-edge to facilitate communication for users located in this challenging region, while cell-center users can communicate directly with the FD base station (BS). We employ a non-orthogonal multiple access pairing scheme and account for system impairments, such as self-interference at the BS and imperfect successive interference cancellation. We derive closed-form expressions for the ergodic rates in both the up-link and down-link communications and extend our analysis to bidirectional communication between cell-center and cell-edge users. Furthermore, we formulate an optimization problem aimed at maximizing the ergodic sum-rate. This optimization involves adjusting the amplitudes and phase-shifts of the STAR-RIS elements and allocating total transmit power efficiently. To gain deeper insights into the achievable rates of STAR-RIS-aided FD systems, we explore the impact of various system parameters through numerical results.

Reconfigurable intelligent surface (RIS) has been identified as a promising technology for future wireless communication systems due to its ability to manipulate the propagation environment intelligently. RIS is a frequency-selective device, thus it can only effectively manipulate the propagation of signals within a specific frequency band. This frequency-selective characteristic can make deploying RIS in wireless cellular networks more challenging, as adjacent base stations (BSs) operate on different frequency bands. In addition, rate-splitting multiple access (RSMA) scheme has been shown to enhance the performance of RIS-aided multi-user communication systems. Accordingly, this work considers a more practical reflection model for RIS-aided RSMA communication systems, which accounts for the responses of signals across different frequency bands. To that end, new analytical expressions for the ergodic sum-rate are derived using the moment generating function (MGF) and Jensen’s inequality. Based on these analytical sum-rate expressions, novel practical RIS reflection designs and power allocation strategies for the RSMA scheme are proposed and investigated to maximize the achievable sum-rate in RIS-assisted multi-cell, multi-band cellular networks. Simple sub-optimal designs are also introduced and discussed. The results validate the significant gains of our proposed reflection design algorithms with RSMA over conventional schemes in terms of achievable sum-rate. Additionally, the power allocation strategy for the RSMA scheme is shown to offer superior performance compared to conventional precoding schemes that do not rely on RSMA.

In contrast to conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been proposed recently to enlarge the serving area from 180° to 360° coverage. This work considers the performance of a STAR-RIS aided full-duplex (FD) non-orthogonal multiple access (NOMA) communication systems. The STAR-RIS is implemented at the cell-edge to assist the celledge users, while the cell-center users can communicate directly with a FD base station (BS). We first introduce new user clustering schemes for the downlink and uplink transmissions. Then, based on the proposed transmission schemes closed-form expressions of the ergodic rates in the downlink and uplink modes are derived taking into account the system impairments caused by the self interference at the FD-BS and the imperfect successive interference cancellation (SIC). Moreover, an optimization problem to maximize the total sum-rate is formulated and solved by optimizing the amplitudes and the phase-shifts of the STAR-RIS elements and allocating the transmit power efficiently. The performance of the proposed user clustering schemes and the optimal STAR-RIS design are investigated through numerical results.

This paper investigates the capacity performance of an uplink fluid antenna system (FAS), in which the base station (BS) is equipped with multiple fluid antennas and each user has a single fluid antenna. We aim to maximize the capacity of the system by optimizing the transmit power, and the user and BS antenna positions. Beginning with simple cases where the number of paths or the number of BS antennas is small, we reveal that the capacity is independent of the antenna positions. Then we give an upper bound on the capacity for the case where the BS has a single fluid antenna. After that, we show that in the optimal case, all users should transmit at the maximum power. Moreover, we propose an alternative algorithm to iteratively optimize the antenna positions at the BS and user sides. When keeping the user antenna positions fixed, the BS antenna positions are updated alternatively using a discrete exhaustive search in the single-user case. By transforming the capacity maximization problem into a difference-of-convex (DC) form, the majorization-minimization (MM) algorithm can also be applied to jointly optimize the BS antenna positions when there is a single user in the system. For the multiuser scenario, the antenna positions at the BS side are optimized utilizing the gradient descent method. We show that the user antenna positions can also be optimized using the discrete exhaustive search or the MM algorithm. Simulation results show that FAS can greatly improve the system capacity compared to traditional fixed-position antenna systems.

In this paper, we investigate the constructive interference (CI) based symbol-level precoding (SLP) design for integrated sensing and communication (ISAC) systems, where a multi-antenna base station (BS) serves multiple single-antenna communication users while simultaneously detecting targets of interest. Specifically, the minimum communication CI scaling factor among the users is maximized under radar performance constraint and power constraint. In order to solve the proposed optimization problem, two groups of approximate feasible domains are adopted to transform the optimization problem into convex. In order to improve the efficiency of the proposed precoding scheme, we adopt a modified Hooke-Jeeves pattern search algorithm for the convex subproblems. We further propose a weighted optimization scheme which considers the tradeoff between radar performance and communication performance as the objective function. By analyzing the Lagrangian function and Karush-Kuhn-Tucker (KKT) condition of the weighted optimization problem, we formulate the corresponding dual problem, which is a simple quadratic programming (QP) problem and can be easily solved. In addition, we further extend the proposed CI precoding scheme from symbol level to block level, in order to be more consistent with the currently used communication systems and achieve better ISAC performance. Extensive simulation results are provided to demonstrate the advantages and the effectiveness of the proposed symbol-scaling based CI-SLP design and CI-based block-level precoding (CI-BLP) design in ISAC systems.

Integrated sensing and communication (ISAC) is seen as a future solution to frequency congestion due to its excellent ability to simultaneously support target sensing and information transmission. To guarantee robust data security and privacy protection, covert communication can be employed in ISAC systems. In this paper, we propose a covert ISAC scheme against collusive wardens. In particular, a dual-function base station transmits the sensing beamforming to continuously sense an aerial target while communicating with a ground receiver with a probability of 0.5 via the communication beamforming. First, we derive a closed-form expression of the detection outage probability of each warden to obtain the global detection outage probability. Under the worst case that the wardens can collusively adjust their detection thresholds to achieve the best detection performance, we jointly optimize the communication and sensing beamformings to maximize the covert transmission rate. To tackle this non-convex problem, unitary-iteration and zero-forcing schemes are proposed to transform it into convex ones via semidefinite relaxation and successive convex approximation, respectively. Numerical results demonstrate the validity of the proposed covert ISAC scheme, which can achieve a better trade-off among communication, sensing and covertness compared to benchmarks.

This paper introduces a novel multi-user mobile edge computing (MEC) scheme facilitated by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and a unmanned aerial vehicle (UAV). Unlike existing MEC approaches, the proposed scheme enables bi-directional offloading, allowing users to concurrently offload tasks to the MEC servers located at ground base station (BS) and UAV with the support of the STAR-RIS. To evaluate the effectiveness of the proposed MEC scheme, we first formulate an optimization problem aiming at maximizing the energy efficiency of the system while ensuring the quality of service (QoS) constraints by jointly optimizing the resource allocation, user scheduling, passive beamforming of the STAR-RIS, and the UAV trajectory. A block coordinate descent (BCD) iterative algorithm designed with the Dinkelbach’s algorithm and the successive convex approximation (SCA) technique is proposed to effectively handle the formulated non-convex optimization problem characterized by significant coupling among variables. Simulation results indicate that the proposed STAR-RIS enhanced UAV-enabled MEC scheme possesses significant advantages in enhancing the system energy efficiency over other baseline schemes including the conventional RIS-aided scheme.

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Besides, we address a more general scenario involving interference and channel estimation overheads, deriving exact users’ outage probabilities and employing a combination of bisection, one-dimensional (1D) search, and SCA algorithms to efficiently and effectively solve max-min optimization problems in both NOMA and OMA systems, significantly enhancing system fairness and computational efficiency. Our numerical results demonstrate that the proposed schemes significantly enhance outage performance over conventional OMA and NOMA benchmarks, even in the presence of interference, confirming their effectiveness in realistic scenarios. The performance of our closed-form and SCA algorithm-based solutions in FAS-assisted NOMA and OMA systems closely approaches that of the optimal solutions, further validated by the effective approximation of users’ outage probabilities in simulations.

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green’s function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method.

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes.

In this paper, we propose a fluid antenna (FA) enabled joint transmit and receive index modulation (FA-JTR-IM) transmission mechanism for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems. By integrating the methodologies of FA and IM, the proposed scheme achieves enhanced spectral efficiency (SE) while requiring only a single radio frequency (RF) chain at both the transmitter and receiver. The proposed scheme offers a low hardware cost and power consumption transmission mechanism for the RIS-aided mmWave communication systems. Specifically, the encoding of information bits encompasses not only the modulated symbol but also the indices of transmit FA positions and receive antennas. To achieve a reliability-complexity trade-off, two types of detectors are introduced for the proposed FA-JTR-IM scheme, including the optimal maximum likelihood (ML) detector and two-step sequential (TSS) detector. Based on the ML detector, we derive the expression for the conditional pair-wise error probability of the proposed FA-JTR-IM scheme. Additionally, we provide the closed-form expressions for the unconditional PEP under the finite-path and infinite-path channel conditions, respectively. Simulation results demonstrate the superiority of the proposed FA-JTR-IM scheme in terms of error performance over its conventional benchmark schemes under the same SE condition.

This paper studies the large-scale cell-free networks where dense distributed access points (APs) serve many users. As a promising next-generation network architecture, cell-free networks enable ultra-reliable connections and minimal fading/blockage, which are much favorable to the millimeter wave and terahertz transmissions. However, conventional beam management with large phased arrays in a cell is very time-consuming in the higher-frequencies, and could be worsened when deploying a large number of coordinated APs in the cell-free systems. To tackle this challenge, the spatial-spectral cell-free networks with the leaky-wave antennas are established by coupling the propagation angles with frequencies. The beam training overhead in this direction can be significantly reduced through exploiting such spatial-spectral coupling effects. In the considered large-scale spatial-spectral cell-free networks, a novel subchannel allocation solution at sub-terahertz bands is proposed by leveraging the relationship between cross-entropy method and mixture model. Since initial access and AP clustering play a key role in achieving scalable large-scale cell-free networks, a hierarchical AP clustering solution is proposed to make the joint initial access and cluster formation, which is adaptive and has no need to initialize the number of AP clusters. After AP clustering, a subchannel allocation solution is devised to manage the interference between AP clusters. Numerical results are presented to confirm the efficiency of the proposed solutions and indicate that besides subchannel allocation, AP clustering can also have a big impact on the large-scale cell-free network performance at sub-terahertz bands.

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a possible technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen due to the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the FAS-assisted multiuser multiple-input multiple-output (MIMO) communications with SAR constraints. In particular, an efficient algorithm is proposed to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) under SAR and FAS constraints. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

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

Fluid antenna multiple access (FAMA) is capable of exploiting the high spatial diversity of wireless channels to mitigate multi-user interference via flexible port switching, which achieves a better performance than traditional multi-input-multi-output (MIMO) systems. Moreover, integrated data and energy transfer (IDET) is able to provide both the wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. In this paper, a FAMA assisted IDET system is studied, where$N$access points (APs) provide dedicated IDET services towards$N$user equipments (UEs). Each UE is equipped with a single fluid antenna. The performance of WDT and WET, i.e., the WDT outage probability, the WET outage probability, the reliable throughput and the average energy harvesting amount, are analysed theoretically by using time switching (TS) between WDT and WET. Numerical results validate our theoretical analysis, which reveals that the number of UEs and TS ratio should be optimized to achieve a trade-off between the WDT and WET performance. Moreover, FAMA assisted IDET achieves a better performance in terms of both WDT and WET than traditional MIMO with the same antenna size.

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

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). Considering the limited capacity of backhaul links, the signals received at the RAPs cannot be sent to the central controller (CC) directly. Instead, a novel metric of result aggregation for IAPS is proposed. Specifically, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at minimizing the probability of error at the CC under communication quality of service constraints, an algorithm of power optimization is proposed. Finally, numerical results validate the positive effect of dedicated sensing symbols and the potential of the proposed IAPS scheme.

In this paper, we investigate a novel intelligent reflecting surface (IRS)-aided multiuser communication system, where a multi-antenna base station (BS) integrated with an IRS simultaneously serves multiple low-mobility and high-mobility users via transmit diversity and active/passive precoding, respectively. Specifically, we exploit IRS's common phase shift to help achieve transmit diversity for high-mobility users without any channel state information (CSI), while incorporating the active/passive precoding design into the IRS-integrated BS to serve low-mobility users with known CSI. Then, we formulate and solve a new problem to minimize the total transmit power at the BS by jointly optimizing the reflect precoding at the IRS and the transmit precoding at the BS to cope with interference among different users. Simulation results validate the performance superiority of our proposed IRS-aided multiuser communication.

Reconfigurable intelligent surfaces (RIS) has emerged as a promising approach for efficiently enhancing communication performance via passive signal reflection. However, in high-mobility scenarios like vehicular communications, the rapidly changing channel presents challenges in acquiring instantaneous channel state information (CSI) for RIS systems with many reflectors, impacting transmission reliability. To overcome this issue, we present an innovative equivalent linear framework equipped with a low-complexity transmitter signal waveform design and receiver signal detection method for downlink communication systems, substantially enhancing stability in fast fading environments. Simulation results indicate that the proposed designs achieve higher communication reliability with low complexity, significantly improving performance in high-mobility scenarios.

In this paper, we investigate a hybrid reconfigurable intelligent surface (RIS) enabled integrated sensing and commu-nication (ISAC) system, in which a hybrid RIS is employed to assist a base station (BS) to sense a specified target, while interacting with multiple communication users (CUs) simultaneously. In particular, a hybrid RIS is introduced such that each of its surface module is able to switch between active and passive modes, reducing the system's power consumption. Subsequently, an optimization problem is formulated with the aim of maximizing the radar output signal-to-noise ratio while satisfying communication requirement for each CU, transmit power constraint for BS and the active RIS elements, by jointly optimizing radar recieve filter, BS's transmit beamforming matrix, RIS reflection coefficients, and the selection matrix that determines the working modes for each unite of the hybrid RIS. Since this design problem is not convex, we propose an alternating optimization based method to solve this problem. Eventually, upon the simulation analysis, we demonstrate that the performance achievable by the proposed scheme is significantly better than the counterparts assisted solely by the active or passive RIS.

This paper studies the performance of a wire-less powered communication network (WPCN) under the non-orthogonal multiple access (NOMA) scheme, where users take advantage of an emerging fluid antenna system (FAS). More precisely, we consider a scenario where a transmitter is powered by a remote power beacon (PB) to send information to the planar NOMA FAS-equipped users through Rayleigh fading channels. After introducing the distribution of the equivalent channel coefficients to the users, we derive compact analytical expressions for the outage probability (OP) in order to evaluate the system performance. Additionally, we present asymptotic OP in the high signal-to-noise ratio (SNR) regime. Eventually, results reveal that deploying the FAS with only one activated port in NOMA users can significantly enhance the WPCN performance compared with using traditional antenna systems (TAS).

Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investigated, where the base station (BS) leverages rate-splitting multiple access (RSMA) for effective interference management and benefits from the support of an AARIS for jointly amplifying and reflecting the BS’s transmit signals. Considering both the non-trivial energy consumption of the active RIS and the limited energy storage of the UAV, we propose an innovative element selection strategy for optimizing the on/off status of active RIS elements, which adaptively and remarkably manages the system’s power consumption. To this end, a resource management problem is formulated, aiming to maximize the system energy efficiency (EE) by jointly optimizing the transmit beamforming at the BS, the element activation, the phase shift and the amplification factor at the active RIS, the RSMA common data rate at users, as well as the UAV’s trajectory. Due to the dynamicity nature of UAV and user mobility, a deep reinforcement learning (DRL) algorithm is designed for resource allocation, utilizing meta-learning to adaptively handle fast time-varying system dynamics. According to simulations, integrating meta-learning yields a notable 36% increase in system EE. Additionally, substituting AARIS for fixed terrestrial active RIS results in a 26% EE enhancement.

In the Internet of Everything (IoE) scenarios, the extensive deployment of devices may result in more stringent power and communication needs. Within this context, we utilize the reconfigurable intelligent surface (RIS) to support the simultaneous wireless information and power transfer (SWIPT) system, whereby the stable transmission of energy and information services can be guaranteed. Specifically, we construct the system model through electromagnetics (EMs), which is based on the scattering-parameter (S-parameter) analysis, for revealing the crucial factors of the practical hardware. Relying on the model, the energy-efficient (EE) maximization problem constrained to the Quality of Services (QoS) is proposed for the users with the framework of co-located receiver (Rx). However, the problem is more intractable due to the introduced channel model. To resolve it, we propose an effective optimization scheme. First, the Neuman series approximation method is adopted to deconstruct the EM transfer model. Then the reformed problem, which includes the variables (i.e., the power splitting ratio, the active beamformer, and the reflection-coefficient matrix), can be addressed through the strategy of alternative optimization (AO). Further, the inner convex approximation (INCA) scheme and Dinkelbach’s algorithm are applied to tackle each subproblem. In the numerical simulation, we demonstrate that the array configuration can influence not only the hardware properties of RIS but also the EE performance of the whole system. What is more, the proposed scheme performs better for the tightly coupled RIS owing to the awareness of the mutual-coupling (MC) effect.

Physical-layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by efficiently providing secret keys in resource-limited network like the Internet of Things (IoT). However, reaching a high key generation rate (KGR) is challenging in applications like smart home or remote area sensing with quasi-static channels. Recently, exploiting reconfigurable intelligent surface (RIS) to induce randomness in quasi-static wireless channels has received significant research interest. However, the inherent spatial correlation among the RIS elements is rarely studied, which can alter the optimum physical-layer key generation (PLKG) approach in terms of KGR and randomness in the key sequence. Specifically, for the first time, in this contribution, we take into account a spatially correlated RIS, which intends to enhance the KGR in a quasi-static medium. Novel closed-form analytical expressions for KGR are derived for the two cases of random phase shift (RPS) and our proposed equal phase shift (EPS) in the RIS elements. We also analyze the correlation between the channel samples to ensure the randomness of the generated secret key sequence. It is shown that the EPS scheme can effectively exploit the inherent spatial correlation between the RIS elements and it leads to a higher KGR compared to the widely used RPS strategy. We further formulate an optimization problem in which we determine the optimal portion of time dedicated to direct and indirect channel estimation, which has never been addressed in previous studies. We show the accuracy and the fast convergence of our sequential convex programming (SCP)-based algorithm and discuss the various parameters affecting spatially correlated RIS-assisted PLKG.

Space-air–ground integrated network (SAGIN) slicing has been studied for supporting diverse applications, which consists of the terrestrial layer (TL) deployed with base stations (BSs), the aerial layer (AL) deployed with unmanned aerial vehicles (UAVs), as well as the space layer (SL) deployed with low earth orbit (LEO) satellites. The capacity of each SAGIN component is limited, and efficient and synergic load balancing (LB) has not been fully considered yet in the exiting literature. For this motivation, we originally propose a priority-based LB scheme for SAGIN slicing, where the AL and SL are merged into one layer, namely non-TL (NTL). First, three typical slices (i.e., high-throughput, low-delay, and wide-coverage slices) are built under the same physical SAGIN. Then, a priority-based cross-layer LB approach is introduced, where the users will have the priority to access the terrestrial BS, and different slices have different offloading priorities. More specifically, the overloaded BS can offload the users of low-priority slices to the NTL preferentially. Furthermore, the throughput, delay, and coverage of the corresponding slices are jointly optimized by formulating a multiobjective optimization problem (MOOP). In addition, due to the independence and priority relationship of TL and NTL, the above MOOP is decoupled into two sub-MOOPs. Finally, we customize a two-layer multiagent deep deterministic policy gradient (MADDPG) algorithm for solving the two subproblems, which first optimizes the user-BS association and resource allocation at the TL, then it determines the UAVs’ position deployment, users-UAV/LEO satellite association, and resource allocation at the NTL. The reported simulation results show the advantages of our proposed LB scheme and show that our proposed algorithm outperforms the benchmarkers.

This paper investigates the optimization of the probabilistically robust transmit beamforming problem with channel uncertainties in the multiuser multiple-input single-output (MISO) downlink transmission. This problem poses significant analytical and computational challenges. Currently, the state-of-the-art optimization method relies on convex restrictions as tractable approximations to ensure robustness against Gaussian channel uncertainties. However, this method not only exhibits high computational complexity and suffers from the rank relaxation issue but also yields conservative solutions. In this paper, we propose an unsupervised deep learning-based approach that incorporates the sampling of channel uncertainties in the training process to optimize the probabilistic system performance. We introduce a model-driven learning approach that defines a new beamforming structure with trainable parameters to account for channel uncertainties. Additionally, we employ a graph neural network to efficiently infer the key beamforming parameters. We successfully apply this approach to the minimum rate quantile maximization problem subject to outage and total power constraints. Furthermore, we propose a bisection search method to address the more challenging power minimization problem with probabilistic rate constraints by leveraging the aforementioned approach. Numerical results confirm that our approach achieves non-conservative robust performance, higher data rates, greater power efficiency, and faster execution compared to state-of-the-art optimization methods.

The clustering unmanned aerial vehicles (UAVs) positioning is significant for preventing unauthorized clustering UAVs from causing physical and informational damages. However, current positioning systems suffer from limited sensing view and positioning range, which result in poor positioning performance. In order to tackle those issues, a novel vehicle-mounted radar-vision clustering UAVs positioning system is developed, which achieves precise, wide-area, and dynamic-view sensing and positioning of the clustering UAVs. Moreover, a matching-based spatiotemporal fusion framework is established to mitigate cross-modal and cross-view spatiotemporal misalignment by adaptively exploiting the cross-modal and cross-view feature correlations. Furthermore, we propose an attention-based spatiotemporal fusion method that achieves a trinity projective attention with the unique structure and task-oriented format for effective feature matching and precise clustering UAVs positioning. Our method also exploited the modality-oriented cross-modal feature and the UAV-motion-oriented cross-view UAV spatiotemporal motion feature.We demonstrate the advantages of our proposed framework and positioning method in our developed clustering UAVs positioning system in practice. Experimental results confirm that our proposed method outperforms the benchmark methods in terms of the positioning precision, especially under the occlusion scenarios. Moreover, ablation studies confirm the effectiveness of each unit of our method.

Full-duplex (FD) technology is gaining popularity for integration into a wide range of wireless networks due to its demonstrated potential in recent studies. In contrast to half-duplex (HD) technology, the implementation of FD in networks necessitates considering internode interference (INI) from various network perspectives. When deploying FD technology in networks, several critical factors must be taken into account. These include self-interference (SI) and the requisite SI cancellation (SIC) processes, as well as the selection of multiple user equipment (UE) per time slot. In addition, INI, including cross-link interference (CLI) and intercell interference (ICI), becomes a crucial issue during concurrent uplink (UL) and downlink (DL) transmission and reception, similar to SI. Since most INIs are challenging to eliminate, a comprehensive investigation that covers radio resource control (RRC), medium access control (MAC), and the physical (PHY) layer is essential in the context of FD network design, rather than focusing on individual network layers and types. This article covers state-of-the-art studies, including protocols and documents from the third-generation partnership project (3GPP) for FD, MAC protocol, user scheduling, and CLI handling. The methods are also compared through a network-level system simulation based on 3-D ray tracing.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case.

User-centric (UC) philosophy is a promising network formation method in light emitting diode enabled visible light communication (VLC) systems. Nevertheless, the nonlinear channel impairments restrict the overall system performance and have not been fully considered in the association structure designing. In this paper, an adaptive user association approach within the UC-cells formation of dense VLC networks is investigated under the consideration of practical nonlinear impairments and adjacent interference. It is mathematically formulated to be an achievable data rate maximization problem by coordinately determining the optimal candidates of access point, clipping ratio and information-carrying power. We divide this mixed combinatorial and non-convex optimization problem into two subproblems and delicately transform them to be binary nonlinear programming and constrained linear programming problems, respectively. In addition, we develop an efficient approach to obtain the local optimal solution with low-computational complexity in an alternating iterative way. Simulation results demonstrate that the proposed scheme has relatively fast convergence and shows robustness to the variation of complex interference patterns and nonlinear impairments. Moreover, it can achieve significant throughput gain as compared with the conventional schemes, demonstrating the prospect and validity of this methodology for dense VLC networks with actual nonlinear devices.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

This paper considers a Gaussian multi-input multi-output (MIMO) wiretap channel with a legitimate transmitter, a legitimate receiver (Bob), an eavesdropper (Eve), and a cooperative jammer. All nodes may be equipped with multiple antennas. Traditionally, the jammer transmits Gaussian noise (GN) to enhance the security. However, using this approach, the jamming signal interferes not only with Eve but also with Bob. In this paper, besides the GN strategy, we assume that the jammer can also choose to use the encoded jammer (EJ) strategy, i.e., instead of GN, it transmits a codeword from an appropriate codebook. In certain conditions, the EJ scheme enables Bob to decode the jamming codeword and thus cancel the interference, while Eve remains unable to do so even if it knows all the codebooks. We first derive an inner bound on the system’s secrecy rate under the strong secrecy metric, and then consider the maximization this bound through precoder design in a computationally efficient manner. In the single-input multi-output (SIMO) case, we prove that although non-convex, the power control problems can be optimally solved for both GN and EJ schemes. In the MIMO case, we propose to solve the problems using the matrix simultaneous diagonalization (SD) technique, which requires quite a low computational complexity. Simulation results show that by introducing a cooperative jammer with coding capability, and allowing it to switch between the GN and EJ schemes, a dramatic increase in the secrecy rate can be achieved. In addition, the proposed algorithms can significantly outperform the current state of the art benchmarks in terms of both secrecy rate and computation time.

Ultra-reliable and low-latency communication (URLLC) is recognized as the most challenging use case for the next generation of wireless networks. Existing research on URLLC is based on the regular pilot (RP) scheme, which is tough to ensure a high transmission rate with stringent latency and reliability requirements due to the impact of finite blocklength, especially in massive connectivity scenarios. In this paper, we propose to use generalized superimposed pilot (GSP) scheme for URLLC transmission in massive multi-input multi-output (mMIMO) systems. Distinguishing from the conventional superimposed pilot (SP) scheme, the GSP scheme eliminates mutual interference between the pilot and data, where the data length is optimized, and the data symbols are precoded to spread over the whole transmission block. With the GSP scheme, we first formulate a weighted sum rate maximization problem by jointly optimizing the data length, pilot power, and data power and then derive closed-form results, including suboptimal data length and achievable rate lower bounds with maximum-ratio combining (MRC) and zero-forcing (ZF) detectors, respectively. Based on the closed-form results, we provide the corresponding iterative algorithms for the MRC and ZF cases where the problems are transformed into geometry program format by using log-function and successive convex approximation methods. Finally, the performance of the RP, SP, and GSP schemes are compared through simulation results, which reflect the superiority and robustness of the GSP scheme in URLLC scenarios.

This paper investigates the achievable region of a K-user discrete memoryless (DM) multiple access wiretap (MAC-WT) channel, where each user transmits both secret and open (i.e., non-confidential) messages. All these messages are intended for the legitimate receiver (Bob), while the eavesdropper (Eve) is only interested in the secret messages. In the achievable coding strategy, the confidential information is protected by open messages and also by the introduction of auxiliary messages. When introducing an auxiliary message, one has to ensure that, on one hand, its rate is large enough for protecting the secret message from Eve and, on the other hand, the resulting sum rate (together with the secret and open message rate) does not exceed Bob’s decoding capability. This yields an inequality structure involving the rates of all users’ secret, open, and auxiliary messages. To obtain the rate region, the auxiliary message rates must be eliminated from the system of inequalities. A direct application of the Fourier-Motzkin elimination procedure is elusive since a) it requires that the number of users K is explicitly given, and b) even for small$K = 3, 4, \ldots $, the number of inequalities becomes extremely large. We prove the result for general K through the combined use of Fourier-Motzkin elimination procedure and mathematical induction. This paper adopts the strong secrecy metric, characterized by information leakage. To prove the achievability under this criterion, we analyze the resolvability region of a K-user DM-MAC channel (not necessarily a wiretap channel). In addition, we show that users with zero secrecy rate can play different roles and use different strategies in encoding their messages. These strategies yield non-redundant (i.e., not mutually dominating) rate inequalities. By considering all possible coding strategies, we provide a new achievable region for the considered channel, and show that it strictly improves those already known in the existing literature by considering a specific example.

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

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms’ efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

This paper investigates the performance of a single-user fluid antenna system (FAS), by exploiting a class of elliptical copulas to describe the dependence structure amongst the fluid antenna positions (ports). By expressing the well-known Jakes’ model in terms of the Gaussian copula, we consider two cases: (i) the general case, i.e., any arbitrary correlated fading distribution; and (ii) the specific case, i.e., correlated Nakagami-m fading. For both scenarios, we first derive analytical expressions for the cumulative distribution function (CDF) and probability density function (PDF) of the equivalent channel in terms of multivariate normal distribution. Then we obtain the outage probability (OP) and the delay outage rate (DOR) to analyze the performance of FAS. By employing the popular rank correlation coefficients such as Spearman’s$\rho $and Kendall’s$\tau $, we measure the degree of dependency in correlated arbitrary fading channels and illustrate how the Gaussian copula can be accurately connected to Jakes’ model in FAS. Our numerical results demonstrate that increasing the size of FAS provides lower OP and DOR, but the system performance saturates as the number of antenna ports increases. In addition, our results indicate that FAS provides better performance compared to conventional single-fixed antenna systems even when the size of fluid antenna is small.

This paper investigates the performance of physical layer security (PLS) in fluid antenna-aided communication systems under arbitrary correlated fading channels. In particular, it is considered that a single fixed-antenna transmitter aims to send confidential information to a legitimate receiver equipped with a planar fluid antenna system (FAS), while an eavesdropper, also taking advantage of a planar FAS, attempts to decode the desired message. For this scenario, we first present analytical expressions of the equivalent channel distributions at the legitimate user and eavesdropper by using copula, so that the obtained analytical results are valid for any arbitrarily correlated fading distributions. Then, with the help of Gauss-Laguerre quadrature, we derive compact analytical expressions for the average secrecy capacity (ASC), the secrecy outage probability (SOP), and the secrecy energy efficiency (SEE) for the FAS wiretap channel. Moreover, for exemplary purposes, we also obtain the compact expression of ASC, SOP, and SEE by utilizing the Gaussian copula under correlated Rayleigh fading channels as a special case. Eventually, numerical results indicate that applying the fluid antenna with only one activated port to PLS can guarantee more secure and reliable transmission, when compared to traditional antenna systems (TAS) exploiting maximal ratio combining (MRC) and antenna selection (AS) under selection combining (SC).

To enable innovative applications and services, both industry and academia are exploring new technologies for sixth generation (6G) communications. One of the promising candidates is fluid antenna system (FAS). Unlike existing systems, FAS is a novel communication technology where its antenna can freely change its position and shape within a given space. Compared to the traditional systems, this unique capability has the potential of providing higher diversity and interference-free communications. Nevertheless, the performance limits of FAS remain unclear as its system properties are difficult to analyze. To address this, we approximate the outage probability and diversity gain of FAS in closed-form expressions. We then propose a suboptimal FAS with$N^{\ast}$ports, where a significant gain can be obtained over FAS with$N^{\ast}-1$ports whilst FAS with$N^{\ast}+1$ports only yields marginal improvement over the proposed suboptimal FAS. In this paper, we also provide analytical and simulation results to unfold the key factors that affect the performance of FAS. Limited to systems with one active radio frequency (RF)-chain, we show that the proposed suboptimal FAS outperforms single-antenna (SISO) system and selection combining (SC) system in terms of outage probability. Interestingly, when the given space is$\frac {\lambda }{2}$, the outage probability of the proposed suboptimal FAS with one active RF-chain achieves near to that of the maximal ratio combining (MRC) system with multiple active RF-chains.

Multiple-input multiple-output (MIMO) system has been the defining mobile communications technology in recent generations. With the ever-increasing demands looming towards the sixth generation (6G), we are in need of additional degrees of freedom that deliver further gains beyond MIMO. To this goal, fluid antenna system (FAS) has emerged as a new way to obtain spatial diversity using reconfigurable position-switchable antennas. Considering the case with more than one ports activated on a 2D fluid antenna surface at both ends, we take the information-theoretic approach to study the achievable performance limits of the MIMO-FAS. First of all, we propose a suboptimal scheme, referred to as QR MIMO-FAS, to maximize the rate at high signal-to-noise ratio (SNR) via joint port selection, transmit and receive beamforming and power allocation. We then derive the optimal diversity and multiplexing tradeoff (DMT) of MIMO-FAS. From the DMT, we highlight that MIMO-FAS outperforms traditional MIMO antenna systems. Further, we introduce a new metric, namelyq-outage capacity, which can jointly consider rate and outage probability. Through this metric, our results indicate that MIMO-FAS surpasses traditional MIMO greatly.

Reconfigurability is a desired characteristic of future communication networks. From a transceiver’s standpoint, this can be materialized through the implementation of fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the transceiver’s antennas. Due to the nature of liquids, FAs can practically take any size and shape, making them both flexible and reconfigurable. In this paper, we deal with the outage probability of FAs under general fading channels, where a port is scheduled based on selection combining. An analytical framework is provided for the performance with and without errors due to post-scheduling delays. We show that although FAs achieve maximum diversity, this cannot be realized in the presence of delays. Hence, a linear prediction scheme is proposed that overcomes delays and restores the lost diversity by predicting the next scheduled port. Moreover, we design space-time coded modulations that exploit the FA’s sequential operation with space-time rotations and code diversity. The derived expressions for the pairwise error probability and average word error rate give an accurate estimate of the performance. We illustrate that the proposed design attains maximum diversity, while keeping a low-complexity receiver, thereby confirming the feasibility of FAs.

Powered by position-flexible antennas, the emerging fluid antenna system (FAS) technology is postulated as a key enabler for massive connectivity in 6G networks. The free movement of antenna elements enables the opportunistic minimization of interference, allowing several users to share the same radio channel without the need of precoding. However, the true potential of FAS is still unknown due to the extremely high spatial correlation of the wireless channel between very close-by antenna positions. To unveil the multiplexing capabilities of FAS, proper (simple yet accurate) modeling of the spatial correlation is prominently needed. Realistic classical models such as Jakes’s are prohibitively complex, rendering intractable analyses, while state-of-the-art approximations often are too simplistic and poorly accurate. Aiming to fill this gap, we here propose a general framework to approximate spatial correlation by block-diagonal matrices, motivated by the well-known block fading assumption and by statistical results on large correlation matrices. The proposed block-correlation model makes the performance analysis possible, and tightly approximates the results obtained with realistic models (Jakes’s and Clarke’s). Our framework is leveraged to analyze fluid antenna multiple access (FAMA) systems, evaluating their performance for both one- and two-dimensional fluid antennas.

Reconfigurable intelligent surface (RIS) has been recognized as a promising technique for the sixth generation (6G) of mobile communication networks. The key feature of RIS is to reconfigure the propagation environment via smart signal reflections. In addition, active RIS schemes have been recently proposed to overcome the deep path loss attenuation inherent in the RIS-aided communication systems. Accordingly, this paper considers the secrecy performance of up-link RIS-aided multiple users multiple-input single-output (MU-MISO) communication systems, in the presence of multiple passive eavesdroppers. In contrast to the existing works, we investigate the impact of the RIS phase shift errors on the secrecy performance. Taking into account the complex environment, where a general Rician channel model is adopted for all the communication links, closed-form approximate expressions for the ergodic secrecy rate are derived for three RIS configurations, namely, i) passive RIS, ii) active RIS, iii) active RIS with energy harvesting (EH RIS). Then, based on the derived expressions, we optimize the phase shifts at the RIS to enhance the system performance. In addition, the best RIS configuration selection is considered for a given target secrecy rate and amount of the power available at the users. Finally, Monte-Carlo simulations are provided to verify the accuracy of the analysis, and the impact of different system parameters on the secrecy performance is investigated. The results in this paper show that, an active RIS scheme can be implemented to enhance the secrecy performance of RIS-aided communication systems with phase shift errors, especially when the users have limited transmission power.

When wideband signals propagate in a rich-scatterer environment, we obtain abundant resolvable multiple transmission paths to form a number of virtual antennas. Therefore, substantial spatial gain can be attained by carefully waveforming in all these resolvable transmission paths without additional antennas. This resultant spatial gain is then exploited for improving the performance of integrated-data-and-energy-transfer (IDET) from a single transmitter to multiple receivers. We aim to maximise the downlink fair-throughput and sum-throughput, while satisfying the energy harvesting requirements by jointly optimising the waveformers at the transmitter and the power splitters at the receivers. A low-complexity fractional-programming (FP) based alternating algorithm is proposed to solve these non-convex optimisation problems. The non-convex wireless energy transfer (WET) constraints are transformed to be convex with a modified quadratic transform (MQT) method. As a result, the stationary points for both the fair-throughput and the sum-throughput maximisation problems are obtained. The numerical results demonstrate the advantage of our proposed algorithm over a minimum-mean-square-error (MMSE) scheme, a zero-forcing (ZF) scheme and a time-reversal (TR) scheme. Simulation results show that the wireless data transfer (WDT) performance of our scheme outperforms the single-input-single-output orthogonal-frequency-division-multiple-access (SISO-OFDMA) when the output direct current (DC) power requirement is high. When we have a practical individual subcarrier power constraint, the WDT performance of our scheme outperforms multiple-input-single-output orthogonal-frequency-division-multiplex-access (MISO-OFDMA).

The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI is available. Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based learning algorithm to design a constraint-aware neural network for optimizing the ISAC precoder. Then, by using the neural precoding network to calculate the reward, we adopt the deep reinforcement learning algorithm to design the port selection and precoder jointly. An advantage actor and critic (A2C) algorithm is proposed to train the policy, in which the actor network uses the pointer network to learn the stochastic policy and the critic network adopts the Long Short-Term Memory (LSTM) encoder architecture to learn the expected reward from the observations. Afterwards, the partial CSI case is addressed, where we propose a masked autoencoder (MAE) induced channel extrapolation for predicting all the CSI to facilitate the joint design. Simulation results demonstrate the promising performance of using FAS for multiuser MIMO and also validate the proposed learning-based scheme.

The emergence of fluid antenna systems (FAS) offers a novel technique for obtaining spatial diversity and leveraging interference fades for spectrum sharing in multiuser scenarios—a paradigm referred to as fluid antenna multiple access (FAMA). Nevertheless, as the number of users increases, the interference mitigation capability diminishes. To overcome this, opportunistic scheduling that prioritizes robust users proves to be an effective method for enhancing FAMA. This paper introduces a resilient decentralized reinforcement learning (RL) approach for opportunistic FAMA (O-FAMA), to autonomously select robust users and the port of each chosen user’s FAS jointly to maximize the network sum-rate. In order to enhance learning efficiency in this multi-agent environment, we propose a novel team-theoretic RL framework that includes a derivative network guiding the multi-agent learning of each solution’s policy networks. Our simulation results confirm the effectiveness of the proposed methodology.

This paper aims to present a simple multiple access scheme for massive connectivity that enables a large number of mobile user equipments (UEs) to occupy the same time-frequency channel without the need of precoding and power control at the base station (BS) and interference cancellation at each UE. The proposed approach does not even require the UEs to know their signal-to-interference ratios (SIRs) and each UE also needs only two radio-frequency (RF) chains to operate. The proposed scheme is inspired by the emerging concept of fluid antenna system (FAS) which enables high-resolution position-switchable antenna to be deployed at each UE. Instead of activating only one port of FAS for reception, each UE activates an ultra massive number of ports to receive the signal. The activated ports are chosen to ensure that the in-phase and quadrature components of the desired signal at the ports are added constructively while the interference signals superimpose randomly. This approach is referred to as compact ultra massive antenna array (CUMA) which can also be realized by deploying a dense, fixed massive antenna array at each UE. We derive the exact probability density function (pdf) of the SIR of a CUMA UE which leads to the data rate analysis. Simulation results demonstrate that even with mutual coupling and under finite scattering, more than 10 UEs can be supported by having a 25×13-port FAS of size 15 cm×8 cm at each UE. Considering quadrature phase shift keying (QPSK), CUMA delivers a network data rate of 10.7 bps per channel use serving 10 UEs at 26 GHz, and the rate is risen to 15.1 bps per channel use if 20 UEs are accommodated at 40 GHz with a 40×21-port FAS at every UE. In the case without mutual coupling and under rich scattering, CUMA can even support hundreds of UEs per channel use.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden’s minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of covert communications between a multi-antenna transmitter and a single-antenna receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions where one antenna is responsible for receiving the transmitted signals and the other one transmits the jamming signals with a varying power to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized in a covert constraint to guarantee the system performance. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on semi-definite relaxation (SDR) method and Dinkelbach’s algorithm is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS, which validates the effectiveness of the proposed algorithm as well as the superiority of STAR-RIS in guaranteeing the covertness of wireless communications.

Fluid antenna system (FAS) is a new flexible antenna technology that offers a new approach to multiple access, referred to as fluid antenna multiple access (FAMA). The performance of FAMA has been investigated but previous results were based on simplified spatial correlation models. In this paper, we will revisit FAMA for the two-user case and study the outage probability by characterizing the joint spatial correlation among the ports. We first derive a closed-form lower bound on the outage probability and reveal that in the absence of spatial correlation, the outage probability of the system decreases exponentially as the number of ports increases. We then show that the channel model can be greatly simplified by focusing upon a limited number of channel variables, allowing us to derive the outage probability using the approximate model. To gain insight, we further approximate the channel model and provide another approximation of the outage probability that is easier to compute. Simulation results validate the approximations and demonstrate that the outage probability decreases with the number of ports but has an error floor unless the antenna size is increased. Also, when the number of ports is fixed, the outage probability initially decreases exponentially with the size but eventually approaches the lower bound.

Fluid antenna system (FAS) represents all forms of movable and non-movable position-flexible antenna system, and opens up the possibility of a new form of modulation schemes. In this paper, we investigate the design of position index modulation (PIM) for FAS for decreasing the bit error rate (BER) while taking advantage of the rate gain in index modulation. We further derive the BER and data rate expressions to assess the achievable performance of PIM. Simulation results are provided to illustrate the performance and some insights are drawn into the impact of both channel estimation accuracy and transmission power.

Due to its capability of channel reconfiguration and enhancement, intelligent reflecting surface (IRS) can be introduced to improve the secrecy rate of non-orthogonal multiple access (NOMA) networks. However, the cost and hardware complexity of full-digital beamforming in existing related studies are high, especially for the systems with massive antennas. This paper studies the robust secure transmission for IRS-aided NOMA networks with cost-effective hybrid beamforming. Specifically, we deploy an IRS to assist the secure transmission from a base station with cost-effective hybrid beamforming to a cell-center user (U1) and a cell-edge user (U2), with the existence of a potential eavesdropper. Two schemes are proposed for guaranteeing the secure transmission of U1 with the perfect and imperfect channel state information (CSI), respectively. With the perfect CSI, the secrecy rate of U1 is maximized subject to the constant modulus constraint and the quality of service (QoS) constraint of U2 via optimizing the hybrid beamforming and phase shifts of IRS. With the imperfect CSI, the achievable rate at U1 is maximized, satisfying its worst-case eavesdropping rate constraint, the constant modulus constraint and the QoS constraint of U2. Because of the non-convexity, we first decompose each problem into two subproblems, respectively. Then, the subproblems are solved via the penalty-based algorithm and the successive convex approximation. Simulation results verify that the two proposed schemes have higher energy efficiency and can boost the security of IRS-aided NOMA networks with perfect and imperfect CSI, respectively.

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neural networks and the success of classical deep neural networks to enhance the learning performance. Specifically, we propose two hybrid quantum-classical neural networks to maximize the sum rate of a downlink system. The first one proposes a quantum neural network employing parameterized quantum circuits that follows a classical convolutional neural network. The classical neural network can be jointly trained with the quantum neural network or pre-trained leading to a fine-tuning transfer learning method. The second one designs a quantum convolutional neural network to better extract features followed by a classical deep neural network. Our results demonstrate the feasibility of the proposed hybrid neural networks, and reveal that the first method can achieve similar sum rate performance compared to a benchmark classical neural network with significantly less training parameters; while the second method can achieve higher sum rate especially in presence of many users still with less training parameters. The robustness of the proposed methods is verified using both software simulators and hardware emulators considering noisy intermediate-scale quantum devices.

Intelligent reflecting surface (IRS) has become a cost-effective solution for constructing a smart and adaptive radio environment. Most previous works on IRS have jointly designed the active and passive precoding based on perfectly or partially known channel state information (CSI). However, in delay-sensitive or high-mobility communications, it is imperative to explore more effective methods for leveraging IRS to enhance communication reliability without the need for any CSI. In this paper, we investigate an innovative IRS-aided multiuser communication system, which integrates an IRS with its aided multi-antenna base station (BS) to simultaneously serve multiple high-mobility users through transmit diversity and multiple low-mobility users through active/passive precoding. In specific, we first reveal that when dynamically tuning the IRS’s common phase-shift shared with all reflecting elements, its passive precoding gain to any low-mobility user remains unchanged. Inspired by this property, we utilize the design of common phase-shift at the IRS for achieving transmit diversity to serve high-mobility users, yet without requiring any CSI at the BS. Meanwhile, the active/passive precoding design is incorporated into the IRS-integrated BS to serve low-mobility users (assuming the CSI is known). Then, taking into account the interference among different users, we formulate and solve a joint optimization problem of the IRS’s reflect precoding and the BS’s transmit precoding, with the aim of minimizing the total transmit power at the BS. Simulation results demonstrate that our proposed co-design of transmit diversity and active/passive precoding in IRS-aided multiuser systems can achieve superior and desirable performance compared to other benchmarks.

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

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of communications between transmitter and receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized as a covert constraint. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR- RIS, an iterative algorithm based on globally convergent version of method of moving asymptotes (GCMMA) is proposed to effectively solve the non-convex optimization problem. Simu-lation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS.

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

The past few years have witnessed a remarkable leap forward in the tactical position of UAV swarm in aerial electronic warfare. Among them, energy-aware packet routing is one of the fundamental problems for cooperation between multiple UAVs to complete combat missions in complex battlefield environments. Recently, deep reinforcement learning (DRL) technique provides a new opportunity to networks related applications, including routing, resource allocation and network access. However, most existing DRL-based routing protocols are difficult to adapt to the changes of network scale, which have weak generalization capabilities and rely on centralized trainers. Thus, these protocols cannot be directly applied to the aerial electronic warfare. In this paper, we propose an adaptive and scalable routing protocol for UAV swarm electronic warfare with graph attention based fully distributed multi-agent reinforcement learning. Besides, a reward function design method based on fuzzy logic is proposed to reduce the probability of abnormal behaviors performed by agents. The simulation results show that our protocol can make effective routing decisions in dynamic wireless multihop networks and enhance the system performances in terms of packet delivery ratio, end-to-end delay and throughput.

The simultaneous wireless information and power transfer (SWIPT) technology assisted by the reconfigurable intelligent surface (RIS) can bring flexibility and stability to the end nodes of the internet of things (IoT) during the deployment. In this paper, we propose a RIS-aided SWIPT system based on a hardware transfer model from the electromagnetic perspective. Particularly, an energy efficiency (EE) maximization problem subject to the quality of service (QoS) demands, power resource budget and circuit restrains is introduced. Furthermore, the active beamforming vectors of the BS and the circuit parameters at the RIS are optimized jointly. The problem can be decomposed into two sub-problems and solved iteratively until convergence. In particular, semi-definite relaxation (SDR), successive convex approximation (SCA), Dinkelbach's algorithm are applied to the solutions of the sub-problems. Numerical results reveal the influences of the various QoS requirements on EE performance. Moreover, the actual generated beams of the BS and the RIS are shown to demonstrate the effectiveness of the proposed optimization strategy.

A desired characteristic of future communication networks is the notion of reconfigurability. For a wireless device, this can be realized through the employment of the so-called fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the device's antennas. Therefore, due to the nature of liquids, an FA can essentially take any size and shape, making them both flexible and reconfigurable. In this paper, we study the outage probability of FAs where the scheduled port, based on selection combining, is subject to scheduling delays. An analytical framework is provided for the performance with and without estimation errors, as a result of post-scheduling delays. We show that even though FAs achieve maximum channel (spatial) diversity, this cannot be attained in the presence of delays.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect channel recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate RIS-assisted channels in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

Fluid antenna system (FAS) is an emerging flexible antenna technology that provides a new way for multiple access. In fluid antenna multiple access (FAMA), each user switches its fluid antenna to the location (i.e., port) in which the interfering users suffer from a deep fade for interference-free communication. Previous work has attempted to understand the interference immunity of FAMA but the results are limited to simplified spatial correlation models. In this paper, we revisit the FAMA system with only two users by characterizing the joint spatial correlation amongst all the ports. Using this model, however, the number of variables determining each channel coefficient scales with that of ports, hence making the analysis intractable. To tackle this, we first show that the channel model could be considerably simplified by taking into account only a few variables, and then derive the outage probability for the considered FAMA system by using the approximated model. Simulation results show that the simplified channel model can quickly approach the exact one and that the outage probability decreases with the number of ports but has an error floor unless the size of fluid antenna is increased.

Due to the ability of channel reconfiguration, intelligent reflecting surface (IRS) can be used to boost the secrecy rate of cognitive non-orthogonal multiple access (NOMA) systems. However, the cost and hardware complexity of full-digital beamforming in existing related studies is high, especially for the systems with massive antennas. In this paper, we investigate the secure transmission for IRS-aided cognitive NOMA systems with cost-effective analog beamforming. The secrecy rate of primary user is maximized subject to the quality of service constraint of secondary user via joint analog and passive beamforming optimization. Owing to the non-convexity, we first transform the problem into two subproblems. Then, each subproblem is tackled via the penalty-based algorithm and the successive convex approximation. Simulation results demonstrate that the proposed transmission scheme has higher energy efficiency and can boost the security of IRS-aided cognitive NOMA systems.

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

This paper investigates the information-theoretic secrecy problem for a K-user discrete memoryless (DM) multiple-access wiretap (MAC-WT) channel. Instead of using the weak secrecy criterion characterized by information leakage rate, we adopt the strong secrecy metric defined by information leakage to better protect the confidential information. We provide an achievable rate region and prove its achievability by providing a coding scheme and analyzing the output statistics in terms of (average) variational distance. We show that the rate region obtained in previous works on the subject is a special case of ours. We also show that the achievability proof in such works is incomplete, because it is assumed that certain inequalities hold while they may not in some cases. We solve this problem by constructing an inequality structure for the rates of all users’ secret and redundant messages, and analyzing the conditions required to maintain this structure.

Fluid antennas located at the user device (UD) can exploit the natural multipath propagation and randomness of the desired data by adjusting its position spatially to find the point at which an interference null occurs. A fast fluid antenna multiple access (f-FAMA) system uses a large antenna array at the base station to transmit each user’s signal from each antenna. The interference is then overcome by a single fluid antenna located at each receiving UD. Previous work has established a channel model for such a f-FAMA system with a technique that estimates the best port for the fluid antenna to receive the signal at every symbol instance. This paper proposes an improved version of the channel model by also taking into consideration the difference in path loss between different ports and also looks into how different fluid antenna architectures effect the performance of an f-FAMA system. Simulation results demonstrate the necessity of considering path loss variations, particularly in scenarios where there are substantial differences in distances from the transmitter to different ports. Additionally, among the three different antenna architectures considered, the performance of the wheel topology antenna is worse than the linear topology and circular topology antennas at shorter reference distances.

Due to its strong flexibility, easy deployment, high maneuverability and extensive connectivity, unmanned aerial vehicle (UAV) swarm has been widely used in the construction of emergency communication network in recent years. Among them, packet routing in a resilient and adaptive manner is one of the fundamental problems for cooperation between multiple UAVs to complete search and rescue tasks. Recently, reinforcement learning (RL) technique has provided a new opportunity for network-related applications, including routing. However, most existing RL-based routing protocols suffer from issues such as local optimum, blind exploration and slow convergence speed. Additionally, the routing protocols based on deep reinforcement learning (DRL) has high computational complexity, making them unsuitable for energy-limited emergency relief scenarios. In this paper, we proposed a Q-learning aided resilient routing protocol with hindsight pre-calculation (QR2HPC) in UAV swarm for the construction of the emergency networks. Firstly, a dynamic exploration and exploitation coefficient is proposed based on the number and speed of neighbors. Secondly, a warm-start mechanism is proposed in the exploration phase that modifies the traditional random next hop selection to a routing approach guided by various indicators. Finally, we introduce a hindsight pre-calculation (HPC) mechanism to improve the robustness of Q-table to traffic flow changes. The experimental results manifest that our protocols can make effective routing decisions in dynamic wireless multi-hop networks, thereby enhancing the system performances in terms of packet delivery ratio, end-to-end delay, throughput and network lifetime.

With the explosive increase in computing demands and the rise of portable wearable devices, the concept of mobile-edge computing (MEC) has emerged and attracted a lot of attention from both academia and industry. Unmanned Aerial Vehicle (UAV) as flexible moving platform has been wide adopted as an edge computing server to help ground users compute their intensive tasks. Although UAV-assisted edge computing is capable to enhance the computing performance, there are still many challenges in this system, including UAV 3D trajectory design, the allocation of UAV computational resources and the communication time allocation between users and UAV. In this article, we try to solve these challenges in a UAV-assisted edge computing system, aiming at minimizing the completion time of computing users’ tasks. Specially, we propose a combination algorithm of the alternating optimization method and the bisection search method to minimize the delay of the whole system. The whole algorithm can be described in two iterative steps. In the first step, with given total number of time slot N assuming each slot with fixed length, we check whether the current N can satisfy the computational demands of the whole system through the alternating optimization algorithm to obtain the computational and time allocation. In the second step, we use the resource allocation results obtained in the first step to choose whether to increase or decrease N via the bisection search method. Then we repeat the first and second steps until we find the the smallest N that best fits the current computational demand. Extensive experimental results demonstrate that our proposed algorithm greatly reduces the users’ task completion time in comparison with traditional benchmarks. In addition, the convergence of the proposed algorithm can be guaranteed.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter-wave (mmWave) system. Specifically, analytical derivations are performed to obtain the closed-form expression of the warden’s minimum detection error probability (DEP) considering the practical assumption. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

Abstract Using reconfigurable fluid antennas, it is possible to have a software‐controlled position‐tuneable antenna to realize spatial diversity and multiplexing gains that are previously only possible using multiple antennas. Recent results illustrated that fast fluid antenna multiple access ( f ‐FAMA) which always tunes the antenna to the position for maximum signal‐to‐interference ratio (SIR) on a symbol‐by‐symbol basis, could support hundreds of users on the same radio channel, all by a single fluid antenna at each user without complex coordination and optimization. The network outage rate, nevertheless, depends on the SIR threshold chosen for each user. Motivated by this, this paper adopts a first‐order approximation to obtain the outage probability expression from which a closed‐form solution is derived for optimizing the SIR threshold in maximizing the network outage rate. Moreover, a closed‐form expression is provided to estimate the number of users in the f ‐FAMA network in which the outage rate begins to plateau. Numerical results show that the proposed SIR threshold achieves near‐maximal outage rate performance.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this article, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several subproblems and solved alternatively due to its nonconvexity. First, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then, an alternative optimization (AO) framework and semidefinite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each subproblem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and, thus, solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions.

With the advances of the Internet of Things and mobile connectivity, location-based services are becoming increasingly popular and continue to enhance our experience. Multiple antennas have been pivotal in providing reliable wireless communications and high-resolution localization. If the antennas of the array are isotropic, then the simplified array manifold determined by the array geometry can be used to estimate the angle of arrival (AOA). However, in the real world, mobile handsets tend to have very limited space, where the practical antennas are equipped on the same ground plane, and the array geometry hardly obeys the rule of half-wavelength spacing. Therefore, a practical antenna couple signals from other antennas, causing a mutual coupling effect. Complex array manifolds are produced on an antenna even if the received signal is propagated through a single path channel. In addition, the irregular radiation pattern of each antenna further impairs the AOA estimation capability. Given the above effects, the simplified array manifold determined by the array geometry can no longer provide precise localization. In this article, we propose a generic array manifold model for both isotropic and practical antennas. We also present an efficient algorithm to enable AOA estimation on practical antennas on the basis of the proposed model and implement it on a 5G phone at a mid-band spectrum with a 100-MHz channel bandwidth. Results reveal the promising performance of the proposed model, with the AOA estimation errors lower than 10° in over 90% of the scenarios.

The Industrial Internet of Things (IIoT) has been viewed as a typical application for the fifth generation (5G) mobile networks. This article investigates the energy efficiency (EE) optimization problem for the Device-to-Device (D2D) communications underlaying unmanned aerial vehicles (UAVs)-assisted IIoT networks with simultaneous wireless information and power transfer (SWIPT). We aim to maximize the EE of the system while satisfying the constraints of transmission rate and transmission power budget. However, the designed EE optimization problem is nonconvex involving joint optimization of the UAV’s location, beam pattern, power control, and time scheduling, which is difficult to tackle directly. To solve this problem, we present a joint UAV location and resource allocation algorithm to decouple the original problem into several subproblems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D)-based algorithm to optimize the beam pattern. We then optimize UAV’s location and power control using the successive convex optimization techniques. Finally, after solving the above variables, the original problem can be transformed into a single-variable problem with respect to the charging time, which is linear and can be tackled directly. Numerical results verify that significant EE gain can be obtained by our proposed algorithm as compared to the benchmark schemes.

It is our pleasure to share with you this Special Issue, which brings together a diverse set of articles dealing with various aspects of semantic and goal-oriented communications, providing a snapshot of research activities in this highly active research area. Wireless communications and networking research has traditionally focused on improving the capacity and throughput of the underlying wireless network. However, recent explosion in data-driven machine learning applications and their reliance on huge datasets collected by edge devices have raised legitimate concerns that the increasing data traffic might soon overwhelm the capacity of current networks despite ongoing efforts to increase their capacity and efficiency. Also, most of the edge intelligence applications impose stringent delay constraints, which cannot be met by naive forwarding of data samples for processing at the receiver end. This made it obvious to researchers in both academia and industry that it is essential to analyze the “value” or “relevance” of collected data, and filter and prioritize the delivery of data based on its value/relevance as well as the wireless channel and network conditions. In this context, data value will be closely connected to the underlying signals and processes that generate the data, e.g., text, image, video, or sensor data, and what the receiver intends to do with the received data. This subjectivity of data value makes semantic and goal-oriented communication a rather elusive research topic, which has led to both an increasingly rich and active area of investigation, but also a controversial one, mainly due to the lack of clear and widely agreed-upon definitions of some of the core concepts and formulations. Despite these disagreements, there is almost unanimous consensus on the importance and potential impact of this line of investigation for the design of future communication systems and networks.

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.

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

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

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.

Dual-function radar and communication (DFRC) has recently drawn significant attention due to its enormous potential. This letter deals with waveform design of DFRC to improve target detectability embedded in clutter environment while guaranteeing the service quality of communication users. Our design objective is to maximize the output signal-to-clutter-plus-noise ratio (SCNR) of multiple-input multiple-output (MIMO) radar, subject to worst-case received symbol errors at communication users. Coordinate descent (CD) as an efficient iteration algorithm is proposed to solve above optimization problem, which splits high-dimensional problem into multiple one-dimensional problem. Furthermore, we introduce Dinkelbach algorithm (DA) to increase rate of convergence, which is an efficient way to reduce complexity. Finally, simulation results are presented to illustrate the effectiveness of the proposed techniques.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ$\epsilon $-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes.

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks.

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.

Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

Fluid antennas offer a novel way to achieve massive connectivity by enabling each user to find a ‘port’ in space where the instantaneous interference undergoes a deep null for multiple access. While this unprecedented capability permits hundreds of users to share the same radio channel, each user needs to switch its best port on a symbol-by-symbol basis, which is impractical. Motivated by this, this paper considers the scenario in which the fluid antenna of each user updates its best port only if the fading channel changes. We refer to this approach asslowfluid antenna multiple access ($s$-FAMA). In this paper, we first investigate the interference immunity of$s$-FAMA through analyzing the outage probability. Then an outage probability upper bound is obtained, from which we shed light on the achievable multiplexing gain of the system and unpack the impacts of various system parameters on the performance. Numerical results reveal that despite having a weaker multiplexing power than the symbol-based,fastFAMA (i.e.,$f$-FAMA), spatial multiplexing of 4 users or more is possible if the users’ fluid antennas have large numbers of ports.

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

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various Mixed Integer Linear Programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, Convolutional Neural Networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the network resources (such as caching space and link bandwidth) competition among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time, whose computation time reaches around 28.9 ms, with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in pseudo real-time.

In this paper, the problem of the deployment and resource management for visible light communication (VLC)-enabled, reconfigurable intelligent surfaces (RISs)-assisted unmanned aerial vehicle (UAV) networks is investigated. In the considered model, UAVs provide terrestrial users with wireless services and illumination simultaneously. Moreover, RISs are utilized to further improve the channel quality between UAVs and users. This joint placement and resource management problem is constructed aiming at acquiring the optimal UAV deployment, RISs phase shift, user and RIS association that satisfies the users’ needs with minimum consumption of the UAVs’ energy. An iterative algorithm that alternately optimizes continuous and binary variables is proposed to solve this mixed-integer programming problem. Specifically, RISs phase shift optimization is solved by phases alignment method and semidefinite program algorithm. Next, the successive convex approximation algorithm is proposed to settle the UAV deployment problem. The user and RIS association variables are relaxed to the continuous ones before adopting the dual method to find the optimal solution. Moreover, a greedy algorithm is proposed as an alternative to RIS association optimization with low complexity. Simulation results show that the proposed two schemes harvest the superior performance of 34.85% and 32.11% energy consumption reduction over the case without RIS, respectively.

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS’s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor.

In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.

One of the main obstacles in full-duplex radios is analog-to-digital converter (ADC) saturation on a receiver due to the strong self-interference (SI). To solve this issue, researchers have proposed two different types of analog self-interference cancellation (SIC) methods—i) passive suppression and ii) regeneration-and-subtraction of SI. For the latter case, the tunable RF component, such as a multi-tap circuit, reproduces and subtracts the SI. The resolutions of such RF components constitute the key factor of the analog SIC. Indeed, they are directly related to how well the SI is imitated. Another major issue in analog SIC is the inaccurate estimation of the SI channel due to the nonlinear distortions, which mainly come from the power amplifier (PA). In this paper, we derive a closed-form expression for the SIC performance of the multi-tap circuit; we consider how the RF components must overcome such practical impairments as digitally-controlled attenuators, phase shifters, and PA. For a realistic performance analysis, we exploit the measured PA characteristics and carry out a 3D ray-tracing-based, system-level throughput analysis. Our results confirm that the non-idealities of the RF components significantly affect the analog SIC performance. We believe our study provides insight into the design of the practical full-duplex system.

Multiple access can be realized by utilizing the spatial moments of deep fades, using fluid antennas. The interference immunity for fluid antenna multiple access (FAMA), nevertheless, comes with the requirement of a large number of ports at each user. To alleviate this, we study the synergy between opportunistic scheduling and FAMA. A large pool of users permits selection of favourable users for FAMA and decreases the outage probability at each selected user. Our objective is to characterize the benefits of opportunistic scheduling in FAMA. In particular, we derive the multiplexing gain of the opportunistic FAMA network in closed form and upper bound the required number of users in the pool to achieve a given multiplexing gain. Also, we find a lower bound on the required outage probability at each user for achieving a given network multiplexing gain, from which the advantage of opportunistic scheduling is illustrated. In addition, we investigate the rate of increase of the multiplexing gain with respect to the number of users in the pool, and derive a tight approximation to the multiplexing gain, expressed in closed form. As a key result of our analysis, we obtain an operating condition on the product of the number of users in the pool and the number of ports at each fluid antenna that ensures a high multiplexing gain. Numerical results demonstrate clear benefits of opportunistic scheduling in FAMA networks, and corroborate our analytical results.

Combining simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) is a feasible scheme to enhance the energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy per user. As the proposed EE maximization problem is non-convex and extremely complex, we propose an efficient alternating optimization (AO) algorithm by decoupling the original problem into three subproblems which are tackled iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Numerical results confirm the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

In this paper, we investigate the uplink secure communication by combining intelligent reflecting surface (IRS) and energy-harvesting (EH) jammer. Specifically, we propose an IRS-aided secure scheme for the uplink transmission via an EH jammer, to fight against the malicious eavesdropper. An energy transfer (ET) phase and an information transmission (IT) phase are proposed in this scheme. In the ET phase, we optimize the phase-shift matrix of IRS to maximize the harvested energy of jammer. In the IT phase, the phase-shift matrix of IRS and time switching factor are jointly optimized to maximize the secrecy rate. To tackle the non-convex problem, we first decompose it into two subproblems to solve by capitalizing on semi-definite relaxation (SDR) and Lagrange duality. Then, the solutions to the original problem can be obtained by alternately optimizing the two subproblems. Simulation results show that the proposed Jammer-IRS assisted secure transmission scheme can significantly enhance the uplink security.

Spectrum map is of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, the existing spectrum map construction schemes mainly depend on spatial interpolation and cannot construct the spectrum map when the measurement data of the target frequency are not obtained. In order to overcome this challenge, an accurate spectrum map construction scheme is proposed by using an intelligent frequency-spatial reasoning approach. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct the highly accurate spectrum maps of the frequencies without spectrum data. A novel autoencoder adapting to the three-dimensional (3D) spectrum data is proposed. Simulation results demonstrate that our proposed scheme is superior to the benchmark schemes in terms of the construction accuracy. Moreover, it is shown that our proposed autoencoder network has a fast convergence speed.

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

This paper investigates a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicles (UAVs) system with non-orthogonal-multiple access (NOMA), where the transmit signals from multiple UAVs to ground users are strengthened through a RIS. An innovative framework is designed to minimize the total power consumption of the system, by jointly optimizing the position of UAVs, RIS reflection coefficients, active beamforming vectors and decoding order. To solve this problem, we first consider the sub-solution of the UAV’s location which can be achieved via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we then yield the closed-form solution for RIS phase coefficients. Subsequently, the transmit power is obtained by the standard convex optimization methods. Finally, a dynamic-order decoding scheme is proposed to optimize the decoding order. Simulation results show that the resource allocation scheme can obviously reduce the total power consumption compared to the benchmark schemes.

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NO-MA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated. An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes.

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and Rayleigh fading. Due to the bottleneck constraint, it is impossible for the relays to inform the destination node of the perfect channel state information (CSI) in each realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also three achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

This article investigates nonorthogonal multiple access (NOMA)-based cloud radio access networks (C-RANs), where edge caching is adopted to cut down the crowdedness of the fronthaul links. We aim to maximize the energy efficiency (EE) by jointly optimizing the power allocation, analog, and digital precoding, which turns out to be an intractable nonconvex optimization problem. To tackle this problem, we first select cluster heads using the selecting cluster-head (SCH) algorithm, where the analog precoding matrix can be resolved by means of maximizing the array gains. Then, the device grouping algorithm is proposed to group devices according to the equivalent channel correlations, and thus, the NOMA devices in the same beam are capable of sharing the same digital precoding vector. Finally, the joint digital precoding design and power allocation algorithm is proposed to decompose the resultant optimization problem into two subproblems and solve them iteratively by applying the Taylor expansion operation and the minimum mean square error (MMSE) detection. Simulation results validate that the proposed NOMA-based C-RANs with a hybrid precoding (HP) scheme can achieve higher spectral efficiency and EE than the traditional orthogonal multiple access (OMA)-based approach and two-stage HP scheme.

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

The integration of device-to-device (D2D) communications with cooperative non-orthogonal multiple access (NOMA) can achieve superior spectral efficiency. However, the mutual interference caused by D2D communications may prevent NOMA from diverging its high spectral efficiency advantage. Meanwhile, the low adaptability of the fixed transmission strategy can decrease the reliability of the cell-edge user (CEU). To further improve the spectral efficiency, we investigate a device-to-multi-device (D2MD) assisted cooperative NOMA system, where two cell-center users (CCUs) and one CEU are paired as a D2MD cluster. Specifically, the base station directly serves the two CCUs while communicating with the CEU via one CCU. Moreover, we propose an adaptive aggregate transmission scheme using dynamic superposition coding, pre-designing the decoding orders and prior information cancellation for the D2MD assisted cooperative NOMA system to enhance the reliability of the CEU. We provide the closed-form expressions for the outage probability, diversity order, outage throughput, ergodic sum capacity, average spectral efficiency, and spectral efficiency scaling over Nakagami-$m$fading channels under perfect and imperfect successive interference cancellation. The numerical results validate the correctness of the analytical derivations and the effectiveness of the proposed scheme.

This paper investigates the secrecy achievable region of multiple access wiretap (MAC-WT) channels where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver (or Bob for brevity) but only the confidential messages need to be protected from the eavesdropper (Eve). We first consider a discrete memoryless (DM) MAC-WT channel where both Bob and Eve jointly decode their interested messages. By using random coding, we find an achievable rate region, within which perfect secrecy can be realized, i.e., all users can communicate with Bob with arbitrarily small probability of error, while the confidential information leaked to Eve tends to zero. Due to the high implementation complexity of joint decoding, we also consider the DM MAC-WT channel where Bob simply decodes messages independently while Eve still applies joint decoding. We then extend the results in the DM case to a Gaussian vector (GV) MAC-WT channel. Based on the information theoretic results, we further maximize the sum secrecy rate of the GV MAC-WT system by designing precoders for all users. Since the problems are non-convex, we provide iterative algorithms to obtain suboptimal solutions. Simulation results show that compared with existing schemes, secure communication can be greatly enhanced by the proposed algorithms, and in contrast to the works which only focus on the network secrecy performance, the system spectrum efficiency can be effectively improved since open messages can be simultaneously transmitted.

With massive deployment, multiple-input–multiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we widen the scope to discuss the surface-wave-assisted RIS that represents a different design dimension in utilizing metasurface technologies. This goes beyond being a passive reflector and can use the surface as an intelligent propagation medium for superb radio propagation efficiency. The third part of this article turns the attention to the fluid antenna, a novel antenna technology that enables a diverse form of reconfigurability that can combine with RIS for ultrahigh capacity, power efficiency, and scalability. This article concludes with a discussion of the potential synergies that can be exploited between MIMO, RIS, and fluid antennas.

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms.

Despite the wide utilization of unmanned aerial vehicles (UAVs), UAV communications are susceptible to eavesdropping due to air-ground line-of-sight channels. Intelligent reflecting surface (IRS) is capable of reconfiguring the propagation environment, and thus is an attractive solution for integrating with UAV to facilitate the security in wireless networks. In this paper, we investigate the secure transmission design for an IRS-assisted UAV network in the presence of an eavesdropper. With the aim at maximizing the average secrecy rate, the trajectory of UAV, the transmit beamforming, and the phase shift of IRS are jointly optimized. To address this sophisticated problem, we decompose it into three sub-problems and resort to an iterative algorithm to solve them alternately. First, we derive the closed-form solution to the active beamforming. Then, with the optimal transmit beamforming, the passive beamforming optimization problem of fractional programming is transformed into corresponding parametric sub-problems. Moreover, the successive convex approximation is applied to deal with the non-convex UAV trajectory optimization problem by reformulating a convex problem which serves as a lower bound for the original one. Simulation results validate the effectiveness of the proposed scheme and the performance improvement achieved by the joint trajectory and beamforming design.

In this paper, we investigate the security enhancement by combining intelligent reflecting surface (IRS) and energy harvesting (EH) jammer for the uplink transmission. Specifically, we propose an IRS-aided secure scheme for the uplink transmission via an EH jammer, to fight against the malicious eavesdropper. The proposed scheme can be divided into an energy transfer (ET) phase and an information transmission (IT) phase. In the first phase, the friendly EH jammer harvests energy from the base station (BS) aided by IRS. We maximize the harvested energy of jammer by obtaining the closed-form solution to the phase-shift matrix of IRS. In the second phase, the user transmits confidential information to the BS while the jamming is generated to confuse the eavesdropper without affecting the legitimate transmission. The phase-shift matrix of IRS and time switching factor are jointly optimized to maximize the secrecy rate. To tackle the non-convex problem, we first decompose it into two sub-problems. The one of IRS can be approximated to convex with fixed time switching factor. Then, the time switching factor can be solved by Lagrange duality. Thus, the solution to the original problem can be obtained by alternately optimizing these two sub-problems. Simulation results show that the proposed Jammer-IRS assisted secure transmission scheme can significantly enhance the uplink security.

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE.

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes.

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced$ K $-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes.

This paper investigates physical layer security for a large-scale WSN with random multiple access, where each fusion center in the network randomly schedules a number of sensors to upload their sensed data subject to the overhearing of randomly distributed eavesdroppers. We propose an uncoordinated random jamming scheme in which those unscheduled sensors send jamming signals with a certain probability to defeat the eavesdroppers. With the aid of stochastic geometry theory and order statistics, we derive analytical expressions for the connection outage probability and secrecy outage probability to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we formulate an optimization problem for maximizing the sum secrecy throughput subject to both reliability and secrecy constraints, considering a joint design of the wiretap code rates for each scheduled sensor and the jamming probability for the unscheduled sensors. We provide both optimal and low-complexity sub-optimal algorithms to tackle the above problem, and further reveal various properties on the optimal parameters which are useful to guide practical designs. In particular, we demonstrate that the proposed random jamming scheme is beneficial for improving the sum secrecy throughput, and the optimal jamming probability is the result of trade-off between secrecy and throughput. We also show that the throughput performance of the sub-optimal scheme approaches that of the optimal one when facing a stringent reliability constraint or a loose secrecy constraint.

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design.

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

Fluid antenna system represents an emerging technology that enables an antenna to switch its physical location in a predefined space. This paper explores the potential of using a single fluid antenna at each mobile user for multiple access, which we refer to it as fluid antenna multiple access (FAMA). FAMA exploits spatial moments of deep fade suffered by the interference to achieve a favourable channel condition for the desired signal, without requiring sophisticated signal processing. We analyze the FAMA network by first deriving the outage probability of the signal-to-interference ratio (SIR) in a double integral form. We then obtain an outage probability upper bound in closed form and an average outage rate lower bound for the FAMA system, with an arbitrary number of interferers, from which the multiplexing gain of FAMA is characterized. We also estimate how large the number of locations is required to achieve a given multiplexing gain using fluid antennas with a given size. Results show that it is possible for FAMA to support hundreds of users using only one fluid antenna of a few wavelengths of space at each user, giving rise to significant gain in the average network outage rate.

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the$ \mathcal {S}$-procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS.

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems.

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput.

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.

This paper investigates the energy efficiency (EE) optimization problem for device-to-device (D2D) communications underlaying non-orthogonal multiple access (NOMA) unmanned aerial vehicles (UAVs)-assisted networks with simultaneous wireless information and power transfer (SWIPT). Our aim is to maximize the energy efficiency of the system while satisfying the constraints of transmission rate and transmission power budget. However, the considered EE optimization problem is non-convex involving joint optimization of the UAV's location, beam pattern, power control and time scheduling, which is difficult to solve directly. To tackle this problem, we develop an efficient resource allocation algorithm to decompose the original problem into several sub-problems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one, and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to optimize the beam pattern. We then optimize UAV's location and power control by applying the successive convex optimization techniques. Finally, after solving the above variables, the original problem is transformed into a single-variable problem with respect to the charging time, which is a linear problem and can be solved directly. Numerical results verify that the significant EE gain can be obtained by our proposed method as compared to the benchmark schemes.

Federated learning (FL) is widely used in privacy sensitive applications for isolated data islands, with the aim of achieving distributed model training, privacy enhancement and model sharing. Electromyographic (EMG) signals are a type of data collected from wearable sensors of subjects which are distributed on multiple devices, highly personalized and play an important role in several applications including prosthetic hand control, sign languages, grasp recognition, etc. This paper utilizes the FL method to detect single and combined finger movements based on EMG signals. The existing research on FL for wearable healthcare faces challenges of variable probability distributions of data, the need for prerequisite knowledge of server model and computational burdens in parameter transmission. To address these problems, this paper proposes a communication efficient FL framework in which each device only needs to transmit the weight matrices of local models to the server for model aggregation. To further reduce the FL transmission delay, a joint learning and resource allocation problem is formulated via optimizing transmit power of each device, time allocation, and user selection. To solve the delay minimization problem, the objective function is first converted to a tractable expression and then the difference of two convex functions programming is adopted. Simulation results using real EMG signals show that the proposed FL framework with personalized training process successfully detects single and combined finger movements for distributed users. Two public EMG datasets with 10 and 15 different finger movements are employed. Over 98% overall test accuracy is achieved in both datasets which surpasses the conventional learning framework by 1.6% and 0.5% on average. Different scenarios with regard to access points and users are investigated and the convexity of the proposed model is discussed.

In this paper, the implementation of a distributed primal-dual algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is first derived in a closed form while considering the impact of wireless factors such as data transmission errors. Based on the derived convergence rate, the optimal transmit power and resource block allocation schemes are designed to minimize the gap between the target solution and the solution achieved by the distributed primal-dual algorithm. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution by up to 52% compared to the distributed primal-dual algorithm without considering imperfect wireless transmission.

In this paper, we propose a compressive channel estimation technique for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multi-objective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed algorithm achieves competitive error performance compared to existing channel estimation algorithms.

Beamforming and non-orthogonal multiple access (NOMA) are two key techniques for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is mounted with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. We aim to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The considered optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in partial offloading pattern, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we propose an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. A resource allocation algorithm for partial offloading pattern is thereby proposed. Simulation results demonstrate that our designed algorithm yields a significant computation performance enhancement as compared to the benchmark schemes.

In this paper, we investigate a deep learning architecture for lightweight online implementation of a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the optimized performance can be achieved based on user equipment’s (UEs’) location-only information. Assuming that each UE is endowed with a limited energy budget, we aim at maximizing the total completed task-input bits (TCTB) of all UEs within a given time slot, through jointly optimizing the RIS reflecting coefficients, the receive beamforming vectors, and UEs’ energy partition strategies for local computing and computation offloading. Due to the coupled optimization variables, a three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the formulated TCTB maximization problem iteratively with guaranteed convergence. The location-only deep learning architecture is then constructed to emulate the proposed BCD optimization algorithm, through which the pilot channel estimation and feedback can be removed for online implementation with low complexity. The simulation results reveal a close match between the performance of the BCD optimization algorithm and the location-only data-driven architecture, all with superior performance to existing benchmarks.

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance.

This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.

In this article, we propose a lightweight security scheme for ensuring both information confidentiality and transmission resiliency in the Internet-of-Things (IoT) communication. A single-antenna transmitter communicates with a half-duplex single-antenna receiver in the presence of a sophisticated multiple-antenna-aided passive eavesdropper and a multiple-antenna-assisted hostile jammer (HJ). A low-complexity artificial noise (AN) injection scheme is proposed for drowning out the eavesdropper. Furthermore, for enhancing the resilience against HJ attacks, the legitimate nodes exploit their own local observations of the wireless channel as the source of randomness to agree on shared secret keys. The secret key is utilized for the frequency hopping (FH) sequence of the proposed communication system. We then proceed to derive a new closed-form expression for the achievable secret key rate (SKR) and the ergodic secrecy rate (ESR) for characterizing the secrecy benefits of our proposed scheme, in terms of both information secrecy and transmission resiliency. Moreover, the optimal power sharing between the AN and the message signal is investigated with the objective of enhancing the secrecy rate. Finally, through extensive simulations, we demonstrate that our proposed system model outperforms the state-of-the-art transmission schemes in terms of secrecy and resiliency. Several numerical examples and discussions are also provided to offer further engineering insights.

Secure localization and velocity estimation are of great importance in Internet-of-Things (IoT) applications and are particularly challenging in the presence of malicious attacks. The problem becomes even more challenging in practical scenarios in which attack information is unknown and anchor node location uncertainties occur due to node mobility and falsification of malicious nodes. This challenging problem is investigated in this article. With reasonable assumptions on the attack model and uncertainties, the secure localization and velocity estimation problem is formulated as an intractable maximum a posterior (MAP) problem. A variational-message-passing (VMP)-based algorithm is proposed to approximate the true posterior distribution iteratively and find the closed-form estimates of the location and velocity securely. The identification of malicious nodes is also achieved in the meantime. The convergence of the proposed VMP-based algorithm is also discussed. Numerical simulations are finally conducted and the results show the VMP-based joint localization and velocity estimation algorithm can approach the Bayesian Cramer Rao bound and is superior to other secure algorithms.

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

This article studies massive access in cell-free massive multi-input multi-output (MIMO)-based Internet of Things and solves the challenging active user detection (AUD) and channel estimation (CE) problems. For the uplink transmission, we propose an advanced frame structure design to reduce the access latency. Moreover, by considering the cooperation of all access points (APs), we investigate two processing paradigms at the receiver for massive access: cloud computing and edge computing. For cloud computing, all APs are connected to a centralized processing unit (CPU), and the signals received at all APs are centrally processed at the CPU. While for edge computing, the central processing is offloaded to part of APs equipped with distributed processing units, so that the AUD and CE can be performed in a distributed processing strategy. Furthermore, by leveraging the structured sparsity of the channel matrix, we develop a structured sparsity-based generalized approximated message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the quantization accuracy of the processed signals is taken into account. Based on the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE scheme is further developed under two paradigms for reduced access latency. Simulation results validate the superiority of the proposed approach over the state-of-the-art baseline schemes. Besides, the results reveal that the edge computing can achieve the similar massive access performance as the cloud computing, and the edge computing is capable of alleviating the burden on CPU, having a faster access response, and supporting more flexible AP cooperation.

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations.

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the three-dimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the user equipment (UEs) can partially offload their computation tasks to the access point (AP). We aim at maximizing the total completed task-input bits (TCTB) of all UEs with limited energy budgets during a given time slot, through jointly optimizing the RIS reflecting coefficients, the AP's receive beamforming vectors, and the UEs' energy partition strategies for local computing and offloading. A three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the non-convex TCTB maximization problem with guaranteed convergence. In order to reduce the computational complexity and facilitate lightweight online implementation of the optimization algorithm, we further construct two deep learning architectures. The first one takes channel state information (CSI) as input, while the second one exploits the UEs' locations only for online inference. The two data-driven approaches are trained using data samples generated by the BCD algorithm via supervised learning. Our simulation results reveal a close match between the performance of the optimization-based BCD algorithm and the low-complexity learning-based architectures, all with superior performance to existing schemes in both cases with perfect and imperfect input features. Importantly, the location-only deep learning method is shown to offer a particularly practical and robust solution alleviating the need for CSI estimation and feedback when line-of-sight (LoS) direct links exist between UEs and the AP.

This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the weighted sum secrecy rate by jointly designing the secure beamforming (BF), the artificial noise (AN), as well as the phase shift of the IRS. An alternating optimization (AO) method is proposed to deal with the formulated non convex problem. In particular, the secure beamforming and AN jamming matrix are optimally designed via the successive convex approximation (SCA) approach for given phase shift, which can be derived by considering the alternating direction method of multiplier (ADMM) and element-wise block coordinate decent (EBCD) methods. Finally, simulation results are presented to show the benefit of the IRS in terms of improving the secrecy performance, when compared to other methods.

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV's energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices' data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives.

The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability.

This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes.

Interference exploitation has recently been shown to provide significant security benefits in multi-user communication systems. In this technique, the known interference is designed to be constructive to the legitimate users and disruptive to the malicious receivers. Accordingly, this paper analyzes the secrecy performance of constructive interference (CI) precoding technique in multi-user multiple-input single-output (MU-MISO) systems with phase-shift-keying (PSK) signals and in the presence of multiple passive eavesdroppers. The secrecy performance of CI technique is comprehensively investigated in terms of symbol error probability (SEP), secrecy sum-rate, and intercept probability (IP). Firstly, new and exact analytical expressions for the average SEP of the legitimate users and the eavesdroppers are derived. In addition, for simplicity and in order to provide more insights, very accurate approximations of the average SEPs are presented in closed-form. Departing from classical Gaussian rate analysis, we employ finite constellation rate expressions to investigate the secrecy sum-rate. In this regard, closed-form analytical expression of the ergodic secrecy sum-rate is obtained. Then, based on the new secrecy sum-rate expression we propose adaptive modulation (AM) scheme with the aim to enhance the secrecy performance. Finally, we present analytical expressions of the IP with fixed and adaptive modulations. The analytical and simulation results demonstrate that, the interference exploitation technique can provide additional up to 17dB gain in the transmit SNR in terms of SEP, and up to 10dB gain in terms of the secrecy sum-rate and the IP, compared to the conventional interference suppression technique. Furthermore, significant performance improvement up to 66% can be achieved with the proposed AM scheme.

Over the past decades, multiple antenna technologies have appeared in many different forms, most notably as multiple-input multiple-output (MIMO), that have transformed wireless communications for extraordinary diversity and multiplexing gains. The various MIMO technologies have been based on placing a number of antennas at some fixed locations which dictate the fundamental limit on the achievable performance. By contrast, this paper envisages the scenario in which the physical position of an antenna can be switched freely to one of the N positions over a fixed-length line space to pick up the strongest signal in the manner of traditional selection diversity. We refer to this system as a fluid antenna system (FAS) for tremendous flexibility in its possible shape and position. The aim of this paper is to study the achievable performance of a single-antenna FAS system with a fixed length and N in arbitrarily correlated Rayleigh fading channels. Our contributions include exact and approximate closed-form expressions for the outage probability of FAS. We also derive an upper bound for the outage probability, from which it is discovered that a single-antenna FAS given any arbitrarily small space can outperform an L-antenna maximum ratio combining (MRC) system if N is large enough. Our analysis also reveals the minimum required size of the FAS, and how large N is considered enough for the FAS to surpass MRC.

This article studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not hold in practice. As a result, a trained model may lead to performance deterioration when the testing network environment changes. To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled data when the testing wireless environment changes. Furthermore, we propose an online algorithm to enhance the adaptation capability of the offline meta algorithm in realistic non-stationary environments. Simulation results demonstrate that the proposed adaptive algorithms achieve much better performance than the direct deep learning algorithm without adaptation in new environments. The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance. In addition, compared to the offline meta-learning algorithm, the proposed online meta-learning algorithm shows superior adaption performance in changing environments.

Relying on the propagation of modulated radio-frequency (RF) signals, we can achieve simultaneous wireless information and power transfer (SWIPT) to support low-power communication devices. In this paper, we proposed a unary coding based SWIPT encoder by considering a practical M-QAM. Markov chains are exploited for characterising coherent binary information source and for modelling the generation process of modulated symbols. Therefore, both mutual information and the average energy harvesting performance at the SWIPT receiver are analysed in semi-closed-form. With the aid of the genetic algorithm, the sub-optimal codeword distribution of the coded information source is obtained by maximising the average energy harvesting performance, while satisfying the requirement of the mutual information. Simulation results demonstrate the advantage of the SWIPT encoder. Moreover, a higher-level unary code and a lower-order M-QAM results in higher WPT performance, when the maximum transmit power of the modulated symbol is fixed.

In this work, we develop a novel channel estimation method using recurrent neural networks (RNNs) for massive multiple-input multiple-output (MIMO) systems. The proposed framework alleviates the need for channel-state-information (CSI) feedback and pilot assignment through exploiting the inherent time and frequency correlations in practical propagation environments. We carry out the analysis using empirical MIMO channel measurements between a 64T64R active antenna system and a state-of-the-art multi-antenna scanner for both mobile and stationary use-cases. We also capture and analyze similar MIMO channel data from a legacy 2T2R base station (BS) for comparison purposes. Our findings confirm the applicability of utilising the proposed RNN-based massive MIMO channel acquisition scheme particularly for channels with long time coherence and hardening effects. In our practical setup, the proposed method reduced the number of pilots used by 25%.

In this paper, we investigate power-efficient constructive interference (CI) exploitation in multi-cell coordination systems. By only sharing channel state information (CSI) among the coordinated base stations (BS)s, we propose a CI-based coordinated beamforming (CBF) scheme to judiciously exploit multiuser interference as a beneficial element rather than strictly mitigating it, while simultaneously suppressing inter-cell interference as a destructive element. Then taking imperfect channel state information (CSI) into consideration, we minimize the total transmission power consumption with multiple users' probabilistic signal-to-interference-and-noise ratio (SINR) requirements, where the users' SINR requirements are guaranteed in a statistical manner. Finally, under the presence of CSI error, simulation results demonstrate that the proposed CI-based CBF scheme consumes much lower transmission power compared to the classical CBF benchmarks, where both intra-cell multi-user and inter-cell interference need to be strictly cancelled as destructive elements. Last but not least, the incurred overhead and computational complexity of the proposed scheme are analytically analyzed, confirming its practicality as a new dimension on multi-cell coordination.

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time.

Reliable and efficient networks are the trend for next-generation wireless communications. Recent improved hardware technologies - known as Large Intelligent Surfaces (LISs) - have decreased the energy consumption of wireless networks, while theoretically being capable of offering an unprecedented boost to the data rates and energy efficiency (EE). In this paper, we use stochastic geometry to provide performance analysis of a realistic two-step user association based millimeter wave (mmWave) networks consisting of multiple users, transmitters and one-hop reflection from a LIS. All the base stations (BSs), users and LISs are equipped with multiple uniform linear antenna arrays. The results confirm that LIS-assisted networks significantly enhance capacity and achieve higher optimal EE as compared to traditional systems when the density of BSs is not large. Moreover, there is a trade-off between the densities of LIS and BS when there is a total density constraint. It is shown that the LISs are excellent supplements for traditional cellular networks, which enormously enhance the average rate and area spectral efficiency (ASE) of mmWave networks. However, when the BS density is higher than the LIS density, the reflected interference and phase-shift energy consumption will limit the performance of LIS-assisted networks, so it is not necessary to employ the LIS devices.

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors' positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target's location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms.

Non-orthogonal multiple access (NOMA) is one of the most significant technologies to meet the demand of high spectral efficiency (SE) in the fifth generation (5G) cellular networks. The utilization of simultaneous wireless information and power transfer (SWIPT) contributes to prolonging the battery life of the mobile users (MUs) and enhancing the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). In this paper, we study the achievable data rate maximization problem for the downlink multi-carrier NOMA (MC-NOMA) network with power splitting (PS)-based SWIPT, in which power allocation and PS control are jointly optimized with the limitation of available power budget as well as the requirement for EH. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of the coupled variables and the inter-user interference. To cope with the problem, a decoupled approach is developed, in which the power allocation and PS control are separated and the corresponding sub-problems are respectively solved through Lagrangian duality method. Furthermore, an alternative approach based on deep learning is proposed, which is capable of effectively obtaining the approximate optimal solution according to the empirical data. Simulation results confirm the effectiveness of the proposed schemes, and demonstrate the superiority of the combination of PS-based SWIPT with MC-NOMA over SWIPT-aided single-carrier NOMA (SC-NOMA) and SWIPT-aided orthogonal multiple access (OMA).

This paper studies the problem of joint edge cache placement and content delivery in cache-enabled small cell networks in the presence of spatio-temporal content dynamics unknown a priori. The small base stations (SBSs) satisfy users' content requests either directly from their local caches, or by retrieving from other SBSs' caches or from the content server. In contrast to previous approaches that assume a static content library at the server, this paper considers a more realistic non-stationary content library, where new contents may emerge over time at different locations. To keep track of spatio-temporal content dynamics, we propose that the new contents cached at users can be exploited by the SBSs to timely update their flexible cache memories in addition to their routine off-peak main cache updates from the content server. To take into account the variations in traffic demands as well as the limited caching space at the SBSs, a user-assisted caching strategy is proposed based on reinforcement learning principles to progressively optimize the caching policy with the target of maximizing the weighted network utility in the long run. Simulation results verify the superior performance of the proposed caching strategy against various benchmark designs.

Mobile edge caching (MEC) and device-todevice (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

Cache-enabled small cells can be an effective solution to deliver contents to mobile users with much lower power and latency. While the trend for getting smaller and denser cells is clear, interference will soon become unmanageable and an obstacle when the number of content requests is massive. Moreover, content request is seldom a spatially homogeneous process due to physical impediments (e.g., buidings) and social activities, which makes resource allocation for content delivery more challenging. In this paper, we consider an ultra-dense network (UDN) in which content requests are served by cache-enabled access nodes which can either be active for delivering contents to users, or inactive to reduce interference and network energy consumption. Our aim is to devise an approach that can locally adapt the caching node density and content caching probabilities to accommodate any arbitrary user density and content request for maximizing the network's successful content delivery probability (SCDP). With a non-homogeneous spatial distribution for user equipments (UEs), we find that user-load, a parameter at the access node, plays a major role in the overall optimization. Simulation results illustrate that the proposed method can obtain superior performance against the considered benchmarks, with up to 150-160% increase, and our optimized solutions effectively adapt to the spatial-dependent user density.

Radio frequency (RF) signals have been relied upon for both wireless information delivery and wireless charging to the massively deployed low-power Internet of Things (IoT) devices. Extensive efforts have been invested in physical layer and medium-access-control layer design for coordinating simultaneous wireless information and power transfer (SWIPT) in RF bands. Different from the existing works, we study the coding controlled SWIPT from the information theoretical perspective with practical transceiver. Due to its practical decoding implementation and its flexibility on the codeword structure, unary code is chosen for joint information and energy encoding. Wireless power transfer (WPT) performance in terms of energy harvested per binary sign and of battery overflow/underflow probability is maximised by optimising the codeword distribution of coded information source, while satisfying required wireless information transfer (WIT) performance in terms of mutual information. Furthermore, a Genetic Algorithm (GA) aided coding design is proposed to reduce the computational complexity. Numerical results characterise the SWIPT performance and validate the optimality of our proposed GA aided unary coding design.

In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the central and the edge networks, we aim to minimize the system energy consumption used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices, which is a mixed-integer and non-convex optimization problem. Based on the methods such as decomposition approach and successive pseudoconvex approach, a tractable solution is proposed via an iterative algorithm. The simulation results show that our proposed solution can achieve great performance gain over conventional schemes using edge or central cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.

A wireless-powered mobile edge computing (MEC) architecture with the cooperation between an access point (AP) and an unmanned aerial vehicle (UAV) is studied in this article. The AP, powered by the grid, is integrated with a high-performance processing server to help compute the user equipment's (UEs') offloaded tasks while also performing high-power laser-like energy charging for the UAV. The UAV serves as (1) an information relay to help the UEs offload/download their computation tasks/results, (2) an energy relay to broadcast energy from the AP to the UEs, as well as (3) an MEC server to help the UEs compute their tasks. We aim at maximizing the weighted sum completed task-input bits (WSCTB) of UEs under the task and time allocation, information-causality, energy-causality, and the UAV's trajectory constraints, by jointly optimizing the task and time allocation as well as the UAV's energy transmit power and trajectory. The formulated WSCTB maximization problem is non-convex, and we propose a three-step block coordinate descending algorithm to address three sub-problems iteratively for obtaining a proper solution. Simulation results show that the UAV's trajectories highly depend on the AP's location and the UEs' weight values. In addition, significant performance improvement is achieved by the proposed algorithm compared to some practical benchmarks.

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting from full base station (BS) coordination, we first propose a fully-coordinated scheme to exploit beneficial effects of both inter-cell and intra-cell interference, based on sharing both channel state information (CSI) and data among the BSs. To reduce the coordination overhead, we then propose a partially-coordinated scheme where only intra-cell interference is designed to be constructive while inter-cell is jointly suppressed by the coordinated BSs. Accordingly, the coordination only involves CSI exchange and the need for sharing data is eliminated. To further reduce the coordination overhead, a third scheme is proposed, which only requires the knowledge of statistical inter-cell channels, at the cost of a slight increase on the transmission power. For all the proposed schemes, imperfect CSI is considered. We minimize the total transmission power in terms of probabilistic and deterministic optimizations. Explicitly, the former statistically satisfies the users' signal-to-interference-plus-noise ratio (SINR) while the latter guarantees the SINR requirements in the worst case CSI uncertainties. Simulation verifies that our schemes consume much lower power compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell coordination.

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.

As the number of communication devices rapidly grows, limited radio resources hardly accommodate the every-increasing tele-traffic. As a remedy, spatial modulation (SM) is capable of modulating additional information onto the index of transmit or receive antennas, which results in substantial improvement of spectrum efficiency. Moreover, radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has attracted tremendous research interest, in order to relieve the energy-thirst of massively deployed low-power communication devices. In this paper, a receive spatial modulation (RSM) aided SWIPT system with finite alphabets is studied, in which three different transmission schemes are proposed, namely the general scheme, the superimposed scheme and the distinct scheme. Furthermore, the performance of these transmission schemes in the RSM aided SWIPT system is theoretically analysed. The energy harvested by the receiver is then maximised by jointly optimising the transmit power of the information signal and the covariance matrix of the energy signal as well as the power splitting ratio, while satisfying the quality of service of the wireless information transfer. At last, simulation results validate our theoretical analysis, while they also demonstrate that the distinct scheme has the best SWIPT performance among these three transmission schemes.

In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only.

In this paper, we investigate a mobile edge computing (MEC) architecture with the assistance of an unmanned aerial vehicle (UAV). The UAV acts as a computing server to help the user equipment (UEs) compute their tasks as well as a relay to further offload the UEs' tasks to the access point (AP) for computing. The total energy consumption of the UAV and UEs is minimized by jointly optimizing the task allocation, the bandwidth allocation and the UAV's trajectory, subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints, and the UAV's trajectory constraints. The formulated optimization problem is nonconvex, and we propose an alternating algorithm to optimize the parameters iteratively. The effectiveness of the algorithm is verified by the simulation results, where great performance gain is achieved in comparison with some practical baselines, especially in handling the computation- intensive and latency-critical tasks.

This paper focuses on joint edge cache placement and content delivery problem at a base station (BS) in the presence of spatio-temporal unknown content dynamics, where the BS can satisfy its users' content demands either directly from its local cache or by fetching from the content server. Unlike the previous works that assume a static content library, we consider a more realistic non-stationary scenario, where new contents are emerging over time at the content library and might be cached at users. We propose that the new contents cached at local users can be utilized by the BS to timely update its flexible portion of cache memory in addition to its routine off-peak main cache update from the content server. We model the caching problem as a non- stationary bandit problem and introduce a user-aided caching algorithm that accounts for the traffic demand variations and the limited caching space at the BS. The proposed algorithm progressively improves the caching policy, with the target of maximizing the weighted content delivery rate to the users in the long run. Simulation results validate that the proposed strategy outperforms various benchmark designs.

In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization.

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.

The number of wireless devices is growing rapidly on a daily basis echoing the increasing number of applications of the Internet of Thing. Facing massive connections and unavoidable interference, how to provide a green communication is a concerning matter. In this regard, nonorthogonal multiple-access (NOMA) is a natural communications technology that can scale with the massive number of simultaneous connections for a limited bandwidth. In this paper, we aim to maximize the energy efficiency (EE) for an NOMA-based cloud radio access network, where sub-6 GHz and millimeter wave bands are used in fronthaul and access links, respectively. In particular, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form. A two-layer algorithm is then proposed. In the outer loop, the ℓ1-norm technique is adopted to transform the nonconvex fronthaul capacity constraint into a convex one, whereas in the inner loop, the weighted minimum mean square error approach is applied. Simulation results indicate that the proposed NOMA scheme can obtain higher EE as well as throughput when compared with orthogonal multiple-access methods.

Massive multiple-input multiple-output systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI.

This paper studies interference exploitation techniques for secure beamforming design in simultaneous wireless information and power transfer in multiple-input single-output systems. In particular, multiuser interference (MUI) and artificially generated noise (AN) signals are designed as constructive to the information receivers (IRs) yet kept disruptive to potential eavesdropping by the energy receivers. The objective is to improve the received signal-to-interference and noise ratio (SINR) at the IRs by exploiting the MUI and AN power in an attempt to minimize the total transmit power. We first propose second-order cone programming-based solutions for the perfect channel state information (CSI) case by defining strong upper and lower bounds on the energy harvesting (EH) constraints. We then provide semidefinite programming-based solutions for the problems. In addition, we also solve the worst case harvested energy maximization problem under the proposed bounds. Finally, robust beamforming approaches based on the above are derived for the case of imperfect CSI. Our results demonstrate that the proposed constructive interference precoding schemes yield huge saving in transmit power over conventional interference management schemes. Most importantly, they show that, while the statistical constraints of conventional approaches may lead to instantaneous SINR as well as EH outages, the instantaneous constraints of our approaches guarantee both constraints at every symbol period.

In this paper, we analyze the sum rate performance of multi-user multiple-input-multiple-output (MU-MIMO) systems, with a finite constellation phase-shift keying (PSK) input alphabet. We analytically calculate and compare the achievable sum rate in three downlink transmission scenarios: 1) without precoding; 2) with zero forcing (ZF) precoding; and 3) with closed form constructive interference (CI) precoding technique. In light of this, new analytical expressions for the average sum rate are derived in the three cases, and Monte Carlo simulations are provided throughout to validate the analysis. Furthermore, based on the derived expressions, a power allocation scheme that can ensure fairness among the users is also proposed. The results in this work demonstrate that the CI strictly outperforms the other two schemes, and the performance gap between the considered schemes increases with the increase in MIMO size. In addition, the CI provides higher fairness and the power allocation algorithm proposed in this paper can achieve maximum fairness index.

Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service.

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. First, we estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high-estimation accuracy. The simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting the promising application of the proposed scheme in large-scale antenna array systems.

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

In this paper, we investigate joint power allocation and time switching (TS) control for energy efficiency (EE) optimization in a TS-based simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) system. Our aim is to optimize the EE of the system whilst satisfying the constraints on maximum transmit power, minimum data rate and minimum harvested energy per-terminal. The considered EE optimization problem is formulated and then transformed according to the duality of broadcast channels (BC) and multiple access channels (MAC). The corresponding non-linear and non-convex optimization problem, involving joint optimization of power allocation and time switching factor, is difficult to solve directly. In order to tackle this problem, we develop a dual-layer algorithm where a convex programming-based Dinkelbach's method is proposed to optimize the power allocation in the inner-layer and an efficient search method is then applied to optimize the TS factor in the outer-layer. Numerical results validate the theoretical findings and demonstrate that significant performance gain over orthogonal multiple access (OMA) scheme in terms of EE can be achieved by the proposed algorithm in a SWIPT-enabled NOMA system.

This paper studies the power-efficient joint radio and computational resource allocation for two near-far mobile devices in a wireless powered mobile edge computing system. To overcome the double-near-far effect for the farther device, cooperative communications in the form of relaying via the nearer device is considered for offloading. The access point (AP)'s total transmit power minimization problem is formulated under the constraints of the computation tasks, which is equivalent to a min-max problem and can be optimally solved by a two-phase method. Numerical results not only show the significant performance improvement of the proposed scheme, but also demonstrate its effectiveness in handling computation-intensive latency-critical (CILC) tasks and resisting the double-near-far effect.

This paper studies the physical (PHY)-layer security performance in full-duplex (FD) multiple- input multiple-output (MIMO) small-cell networks. Here, we take into account (i) residual self- interference (SI) over Rician fading channels, and (ii) mutual-interference (MI) under successive interference cancellation (SIC) mechanism. Considering linear zero-forcing (ZF) beamforming, the downlink (DL) and uplink (UL) average secrecy rates under both scenarios of passive and colluding eavesdropping are derived. Our findings indicate that the FD functionality can provide substantial improvements in the PHY-layer security performance, especially with the aid of MIMO communications and interference cancellation solutions.

Full-duplex (FD) has emerged as a disruptive solution for improving the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in the context of cellular networks, is however largely limited by the mutual interference (MI) between the downlink (DL) and uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic analysis of FD enabled cloud radio access network (CRAN) with finite user- centric cooperative clusters. Contrary to the most existing theoretical studies of C-RAN, we explicitly take into consideration non-isotropic fading channel conditions, and finite-capacity fronthaul links. Accordingly, we develop analytical expressions for the FD C-RAN DL and UL SEs. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient- capacity fronthaul links and advanced interference cancellation capabilities.

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in 5G communication systems and the Internet of Things. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely transmit covariance matrices and TS ratios, leads to an EE problem which is nonconvex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a suboptimal min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner-layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide two iterative resource allocation schemes under fixed auxiliary variables for solving the dual MAC problem. A subgradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution toward 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, and so on. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

We provide a theoretical study of physical (PHY)-layer security performance in full-duplex (FD) small-cell networks. Here, the multi-antenna base stations (BSs) and user equipments (UEs) follow from the homogeneous Poisson point process-based abstraction model. To facilitate FD communications, we take into account: 1) successive interference cancellation capability at the UE side via guard regions of arbitrary radii and 2) residual self-interference at the BS side using Rician fading distribution with arbitrary statistics. We investigate the small-cell network PHY-layer security performance in the presence of a Poisson field of eavesdroppers, under the different scenarios of passive and colluding eavesdropping. Considering linear zero-forcing beamforming, we characterize the downlink and the uplink ergodic secrecy rates and derive closed-form expressions for the different useful and interference signals statistics. In certain special cases of interest, we apply non-linear curve-fitting techniques to large sets of (exact) theoretical data in order to obtain closed-form approximations for the different ergodic rates and ergodic secrecy rates under consideration. Our findings indicate that the FD functionality, in addition to enhancing the spectral efficiency, can significantly improve the PHY-layer security performance, especially with the aid of multi-antenna communications and interference cancellation schemes.

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while the minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO's problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations.

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.

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

In this paper, a fundamental study of energy efficiency (EE) optimization for coordinated multi-point (CoMP) simultaneous wireless information and power transfer (SWIPT) heterogeneous networks (HetNets) is provided. We aim to optimize the EE while satisfying certain quality-of-service requirements in regard to transmission rate and energy harvesting at both the macro cell and small cells. The corresponding joint beamforming and power allocation in the presence of intra- and inter-cell interference constitutes an EE maximization problem that is non-convex, and hence, very challenging to solve. In order to solve this problem, we propose to separate the beamforming design and power allocation processes. First, we adopt linear zero-forcing (ZF) beamforming to suppress the multi-user interference from both the energy harvesting users (EH-UEs) as well as the information decoding UEs (ID-UEs), thus transforming the HetNet under consideration to a virtual point-to-point system. An efficient power allocation algorithm is then developed to maximize the corresponding EE. On the other hand, the ZF strategy does not utilize the notion that interference benefits the EH-UEs. As a result, we propose a partial ZF approach by differentiating the EH-UEs and ID-UEs in order to further improve the EE. Our findings show that the EE can be significantly improved through the integration of CoMP-SWIPT in HetNets.

Conventionally, interference and noise are treated as catastrophic elements in wireless communications. However, it has been shown recently that exploiting known interference constructively can contribute to signal detection ability at the receiving end. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). The scenario considered here is a multiple-input single-output wiretap channel with multiple Eves. This paper starts from AN design without any knowledge of Eve's CSI, builds with solutions with statistical CSI up to full CSI. Both perfect and imperfect channel information have been considered, in particular, with different extent of Eves' channel responses. The main objective is to improve the receive signal-to-interference and noise ratio at IR through exploitation of AN power in an attempt to minimize the total transmit power, while hindering detection at the Eves. Numerical simulations demonstrate that the proposed constructive AN precoding approach yields superior performance over conventional AN schemes in terms of transmit power. Critically, they show that, while the statistical constraints of conventional approaches may lead to instantaneous IR outages and security breaches from the Eves, the instantaneous constraints of our approach guarantee both IR performance and secrecy at every symbol period.

This paper focuses on edge caching in dense heterogeneous cellular networks, in which small base stations (SBSs) with limited cache size store the popular contents, and massive multiple-input multiple-output (MIMO)-aided macro base stations provide wireless self-backhaul when SBSs require the non-cached contents. Our aim is to address the effects of cell load and hit probability on the successful content delivery (SCD) and present the minimum required base station density for avoiding the access overload in an arbitrary small cell and backhaul overload in an arbitrary macrocell. The achievable rate of massive MIMO backhaul without any downlink channel estimation is derived to calculate the backhaul time, and the latency is also evaluated in such networks. The analytical results confirm that hit probability needs to be appropriately selected in order to achieve SCD. The interplay between cache size and SCD is explicitly quantified. It is theoretically demonstrated that when non-cached contents are requested, the average delay of the non-cached content delivery could be comparable to the cached content delivery with the help of massive MIMO-aided self-backhaul, if the average access rate of cached content delivery is lower than that of self-backhauled content delivery. Simulation results are presented to validate our analysis.

This paper studies a mobile edge computing (MEC) system in which two mobile devices are energized by the wireless power transfer (WPT) from an access point (AP) and they can offload part or all of their computation-intensive latency-critical tasks to the AP connected with an MEC server or an edge cloud. This harvest-then-offload protocol operates in an optimized time-division manner. To overcome the doubly near-far effect for the farther mobile device, cooperative communications in the form of relaying via the nearer mobile device is considered for offloading. Our aim is to minimize the AP's total transmit energy subject to the constraints of the computational tasks. We illustrate that the optimization is equivalent to a min-max problem, which can be optimally solved by a two-phase method. The first phase obtains the optimal offloading decisions by solving a sum-energy-saving maximization problem for given an energy transmit power. In the second phase, the optimal minimum energy transmit power is obtained by a bisection search method. Numerical results demonstrate that the optimized MEC system utilizing cooperation has significant performance improvement over systems without cooperation.

By taking a variety of realistic hardware imperfections into consideration, we propose an optimal power allocation (OPA) strategy to maximize the instantaneous secrecy rate of a cooperative wireless network comprised of a source, a destination, and an untrusted amplify-and-forward relay. We assume that either the source or the destination is equipped with a large-scale multiple antennas' system, while the rest are equipped with a single antenna. To prevent the untrusted relay from intercepting the source message, the destination sends an intended jamming noise to the relay, which is referred to as destination-based cooperative jamming. Given this system model, novel closed-form expressions are presented in the high signal-to-noise ratio regime for the ergodic secrecy rate and the secrecy outage probability. We further improve the secrecy performance of the system by optimizing the associated hardware design. The results reveal that by beneficially distributing the tolerable hardware imperfections across the transmission and reception radio-frequency front ends of each node, the system's secrecy rate may be improved. The engineering insight is that equally sharing the total imperfections at the relay between the transmitter and the receiver provides the best secrecy performance. Numerical results illustrate that the proposed OPA together with the most appropriate hardware design significantly increases the secrecy rate.

Uplink/downlink (UL/DL) decoupling promises more flexible cell association and higher throughput in heterogeneous networks (HetNets), however, it hampers the acquisition of DL channel state information (CSI) in time-division-duplex systems due to different base stations (BSs) connected in UL/DL. In this paper, we propose a novel data-aided (DA) channel estimation scheme to address this problem by utilizing decoded UL data to exploit CSI from received UL data signal in decoupled HetNets where a massive multiple-input multiple-output BS and dense small cell BSs are deployed. We analytically estimate bit error ratio (BER) performance of UL decoded data, which are used to derive an approximated normalized mean square error (NMSE) expression of the DA minimum mean square error (MMSE) estimator. Compared with the conventional least square and MMSE, it is shown that NMSE performances of all estimators are determined by their signal-to-noise ratio (SNR)-like terms and there is an increment consisting of UL data power, UL data length, and BER values in the SNR-like term of DA method, which suggests DA method outperforms the conventional ones in any scenarios. Higher UL data power, longer UL data length, and better BER performance lead to more accurate estimated channels with DA method. Numerical results verify that the analytical BER and NMSE results are close to the simulated ones and a remarkable gain in both NMSE and DL rate can be achieved by DA method in multiple scenarios with different modulations.

Full-duplex (FD) wireless has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference mitigation. The FD versus half-duplex (HD) SE gain in cellular networks is, however, largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process-based abstraction model of multi-antenna radio units and user equipments. We consider different network- and user-centric approaches toward the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo simulations. According to simulations using parameters of a state-of-the-art system, significant FD versus HD C-RAN SE gains can be achieved in the presence of advanced interference cancellation capabilities and sufficient-capacity fronthaul links.

This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave (mm-wave) base stations. Different from the existing works which only consider a single antenna at each base station, the optimal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mm-wave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mm-wave systems reveal an interesting tradeoff between caching capacity and density for the mm-wave system to achieve similar performance as the sub-6 GHz system.

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.

In this paper, the cooperative caching strategies are developed for a typical cache-enabled small cell network under heterogeneous file and network settings, where the neighboring base stations are enabled to collaborate to share the cached content. To make full usage of the content diversity in the caches, maximum distance separable (MDS) codes are used for content restructuring. The content placement and the cooperation policy among the neighboring base stations are jointly optimized to minimize the long-term average user attrition (UA) cost for fetching content from external storage subject to the cache capacity constraints. In addition to the unicast based cooperative caching scheme, a compound caching strategy, namely multicast-aware cooperative caching assuming fixed and dynamic cooperative policies, respectively, is developed to combine the merits of multicast-aware content delivery and cooperative content sharing. Mathematical analysis and simulation results are presented to illustrate the advantages of MDS coded cooperative caching strategies in terms of reducing the backhaul requirements.

This paper studies the performance of cache-enabled dense small cell networks consisting of multi- antenna sub-6 GHz and millimeter-wave base stations. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. In general, the optimal content placement is unknown when the base stations have multiple antennas. Then we propose a simple yet effective probabilistic content placement scheme to maximize the successful content delivery probability, which could balance caching both the most popular contents and achieving content diversity. Numerical results demonstrate that our proposed content placement scheme yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and millimeter-wave systems reveal an interesting tradeoff between caching capacity and base station density for the millimeter-wave system to achieve similar performance as the sub-6 GHz system.

This paper focuses on the energy coverage in wireless powered sub-6 GHz and millimeter wave (mmWave) dense cellular networks, where mobile devices harvest RF energy from sub-6 GHz or mmWave base stations (BSs). The expressions for energy coverage probability in sub-6 GHz and mmWave tiers are respectively derived. The comparisons between sub-6 GHz and mmWave RF energy harvesting are analyzed. In particular, we provide the sufficient conditions for the case that wireless energy harvesting in mmWave tier is better than that in sub-6 GHz tier. Furthermore, in hybrid cellular networks with mode selection mechanism, the probability that a mobile device selects a sub-6 GHz BS or mmWave BS for wireless power transfer is also theoretically obtained.

Recent advances in interference exploitation showed that exploiting knowledge of interference constructively can improve the receive signal-to-interference and noise ratio (SINR) at the destination. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). A multiple-input single-output (MISO) wiretap channel with multiple eavesdroppers scenario has been investigated taking both perfect and imperfect channel information into consideration. The main objective is to improve the receive SINR at the IR through exploitation of AN power in an attempt to minimize the total transmit power, while confusing the Eves.

In this work, we investigate the physical layer security (PHYLS) performance of full-duplex (FD) cellular networks, where the downlink (DL) and uplink (UL) occur over the same radio-frequency (RF) resources. Here, the locations of the base stations (BSs) and mobile terminals (MTs) are drawn from stationary Poisson point processes (PPPs). Moreover, the eavesdroppers (EDs) locations are unknown to the network, and are thus modeled from an independent PPP. We characterize the signal-to-interference-plus-noise ratio (SINR) distributions at the reference BS, MT, and most malicious EDs. Accordingly, we develop explicit expressions for the secrecy rates in both UL and DL of the FD cellular network under consideration. Our finding show that the choice of FD versus HD operation, in addition to improving the spectral efficiency, can enhance the secrecy rate, particularly for ultra-dense deployments.

In this paper, we provide joint subcarrier assignment and power allocation schemes for quality- of-service (QoS)-constrained energy-efficiency (EE) optimization in the downlink of an orthogonal frequency division multiple access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering underlay transmission, where spectrum- efficiency (SE) is fully exploited, the EE solution involves tackling a complex mixed-combinatorial and non-convex optimization problem. With appropriate decomposition of the original problem and leveraging on the quasi-concavity of the EE function, the problem can be efficiently solved. On the other hand, the inherent inter-tier interference from spectrum underlay access may degrade EE particularly under dense small-cell deployment and large bandwidth utilization. We therefore develop a novel resource allocation approach based on the concepts of spectrum overlay access and resource efficiency (RE) (normalized EE-SE trade-off). Specifically, the optimization procedure is separated where the macro- cell optimal RE and the corresponding bandwidth is first determined, then the EE of small-cells utilizing the remaining spectrum is maximized. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation schemes can approach the optimal EE with each strategy being superior under certain system settings.

In this paper, we investigate joint antenna selection and spatial switching (SS) for energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigen-channel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigen-channel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming. We then provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (MIMO-BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely, transmit covariance matrices and TS ratios, leads to a EE problem which is non-convex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner- layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide an iterative resource allocation scheme under fixed auxiliary variables for solving the dual MAC problem. A sub-gradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

One of key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a spatially dynamic power control solution for mitigating the cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed D2D power control policy is rather flexible including the special cases of no D2D links or using maximum transmit power. Under the considered power control, an analytical approach is developed to evaluate the spectral efficiency (SE) and energy efficiency (EE) in such networks. Thus, the exact expressions of SE and EE for a cellular user or D2D transmitter are derived, which quantify the impacts of key system parameters such as massive MIMO antennas and D2D density. Numerical results corroborate our analysis and show that the proposed power control solution can efficiently mitigate interference between the cellular and D2D tier.

We consider the case where a reference full-duplex (FD) node (e.g., base station), equipped with arbitrary number of transmit/receive antennas, utilizes generalized linear beamformers to simultaneously communicate with multiple FD radios (e.g., user equipments). The fading coefficients for the residual self-interference (SI) channels are drawn from the complex Gaussian distribution with arbitrary mean and variance. Here, it is not possible to directly derive the distribution of the bidirectional channel power gain. As a result, we adopt the method of moments in order to provide a new Gamma approximation for the residual SI distribution over FD multi-user MIMO Rician fading channels. The proposed theorem holds under arbitrary linear precoder/decoder design, number of antennas and streams, and SI cancellation capability. The validity of the theoretical findings is confirmed via extensive simulations of the entire transmit/receive processing chain.

This study studies a multi‐pair massive multiple‐input multiple‐output (MIMO) relaying network, where multiple pairs of users are served by a single relay station with a large number of antennas, and the amplify‐and‐forward protocol and zero‐forcing (ZF) beamforming are used at the relay. The authors investigate the ergodic achievable rates for the users and obtain tight approximations in closed form for finite number of antennas. The rate performance and power efficiency are studied based on the analytical results for asymptotic scenarios, and the effect of scaling factors of transmit powers for users and relay are discussed. The closed‐form expressions enable us to determine the optimal user scheduling which maximizes the ergodic sum‐rate for the selected pairs. A simplified user scheduling algorithm is proposed which greatly reduces the average complexity of the optimal use pair search without any rate loss. Moreover, the complexity reduction for the proposed algorithm increases nonlinearly with the increase of the number of user pairs, which indicates that the simplified scheduling algorithm has notable advantages when the number of users is increased. The tightness for the analytical approximations and the superiority of the proposed algorithm are verified by Monte‐Carlo simulation results.

This paper focuses on wireless powered 5G dense cellular networks, where base station (BS) delivers energy to user equipment (UE) via the microwave radiation in sub-6 GHz or millimeter wave (mmWave) frequency, and UE uses the harvested energy for uplink information transmission. By addressing the impacts of employing different numbers of antennas and bandwidths at lower and higher frequencies, we evaluate the amount of harvested energy and throughput in such networks. Based on the derived results, we obtain the required small cell density to achieve an expected level of harvested energy or throughput. Also, we obtain that when the ratio of the number of sub-6-GHz BSs to that of the mmWave BSs is lower than a given threshold, UE harvests more energy from an mmWave BS than a sub-6-GHz BS. We find how many mmWave small cells are needed to perform better than the sub-6-GHz small cells from the perspectives of harvested energy and throughput. Our results reveal that the amount of harvested energy from the mmWave tier can be comparable to the sub-6-GHz counterpart in the dense scenarios. For the same tier scale, mmWave tier can achieve higher throughput. Furthermore, the throughput gap between different mmWave frequencies increases with the mmWave BS density.

One of the key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a spatially dynamic power control solution for mitigating the cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed D2D power control policy is rather flexible, including the special cases of no D2D links or using maximum transmit power. Under the considered power control, an analytical approach is developed to evaluate the spectral efficiency (SE) and energy efficiency (EE) in such networks. Thus, the exact expressions of SE for a cellular user or D2D transmitter are derived, which quantify the impacts of key system parameters, such as massive MIMO antennas and D2D density. Moreover, the D2D scale properties are obtained, which provide the sufficient conditions for achieving the anticipated SE. Numerical results corroborate our analysis and show that the proposed power control solution can efficiently mitigate interference between the cellular and the D2D tier. The results demonstrate that there exists the optimal D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of a cellular user can be comparable with that of a D2D user.

We provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the residual self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. In order to facilitate bi-directional wireless functionality, we adopt: 1) in the downlink (DL), a linear zero-forcing (ZF) with SI-nulling precoding scheme at the FD base stations and 2) in the uplink (UL), an SI-aware fractional power control mechanism at the FD mobile terminals. Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the FD baseline single-input single-output system can be overcome via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check, since we show that, under state-of-the-art system parameters, the spectral efficiency gain of FD massive MIMO over its half-duplex counterpart is largely limited by the cross-mode interference between the DL and the UL. In point of fact, the anticipated twofold increase in SE is shown to be only achievable when the number of antennas tends to be infinitely large.

Ultra-dense cloud radio access networks (C-RANs) are an example of the architectures that will be critical components of the next-generation wireless systems. In a C-RAN architecture, an amorphous cellular framework, where each user connects to a few nearby remote radio heads (RRHs) to form its own cell, appears to be promising. In this paper, we study the ergodic capacity of such amorphous cellular networks at high signal-to-noise ratios (SNRs) where we model the distribution of the RRHs by a Poisson point process. We derive tractable approximations of the ergodic capacity at high-SNRs for arbitrary antenna configurations, and tight lower bounds for the ergodic capacity when the numbers of antennas are the same at both ends of the link. In contrast to prior works on distributed antenna systems, our results are derived based on random matrix theory and involve only standard functions which can be much more easier evaluated. The impact of the system parameters on the ergodic capacity is investigated. By leveraging our analytical results, we propose two efficient scheduling algorithms for RRH selection for energy-efficient transmission. Our algorithms offer a substantial improvement in energy efficiency compared with the strategy of connecting a fixed number of RRHs to each user.

This paper considers simultaneous wireless information and power transfer in a multiple-input single-output downlink system consisting of one multi-antenna transmitter, one single-antenna information receiver, multiple multi-antenna eavesdroppers (Eves), and multiple single-antenna energy-harvesting receivers (ERs). The main objective is to keep the probability of the legitimate user's achievable secrecy rate outage as well as the ERs' harvested energy outage caused by channel state information uncertainties below some prescribed thresholds. As is well known, the secrecy rate outage constraints present a significant analytical and computational challenge. Incorporating the energy harvesting outage constraints only intensifies that challenge. In this paper, we address this challenging issue using convex restriction approaches which are then proved to yield rank-one optimal beamforming solutions. Numerical results reveal the effectiveness of the proposed schemes.

This paper brings the novel idea of paying the utility to the winning agents in terms of some physical entity in cooperative communications. Our setting is a secret two-way communication channel where two transmitters exchange information in the presence of an eavesdropper. The relays are selected from a set of interested parties, such that the secrecy sum rate is maximized. In return, the selected relay nodes' energy harvesting requirements will be fulfilled up to a certain threshold through their own payoff so that they have the natural incentive to be selected and involved in the communication. However, relays may exaggerate their private information in order to improve their chance to be selected. Our objective is to develop a mechanism for relay selection that enforces them to reveal the truth since otherwise they may be penalized. We also propose a joint cooperative relay beamforming and transmit power optimization scheme based on an alternating optimization approach. Note that the problem is highly non-convex, since the objective function appears as a product of three correlated Rayleigh quotients. While a common practice in the existing literature is to optimize the relay beamforming vector for given transmit power via rank relaxation, we propose a second-order cone programming-based approach in this paper, which requires a significantly lower computational task. The performance of the incentive control mechanism and the optimization algorithm has been evaluated through numerical simulations.

Caching at the wireless edge is a promising approach to dealing with massive content delivery in heterogeneous wireless networks, which have high demands on backhaul. In this paper, a typical cache-enabled small cell network under heterogeneous file and network settings is considered using maximum distance separable (MDS) codes for content restructuring. Unlike those in the literature considering online settings with the assumption of perfect user request information, we estimate the joint user requests using the file popularity information and aim to minimize the long-term average backhaul load for fetching content from external storage subject to the overall cache capacity constraint by optimizing the content placement in all the cells jointly. Both multicast-aware caching and cooperative caching schemes with optimal content placement are proposed. In order to combine the advantages of multicast content delivery and cooperative content sharing, a compound caching technique, which is referred to as multicast-aware cooperative caching, is then developed. For this technique, a greedy approach and a multicast-aware in-cluster cooperative approach are proposed for the small-scale networks and large-scale networks, respectively. Mathematical analysis and simulation results are presented to illustrate the advantages of MDS codes, multicast, and cooperation in terms of reducing the backhaul requirements for cache-enabled small cell networks.

In this paper, we investigate joint antenna selection and spatial switching for quality-of-service-constrained energy efficiency (EE) optimization in a multiple-input multiple-output simultaneous wireless information and power transfer system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigenchannel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigenchannel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigenchannel assignment and power allocation, and low-complexity multi-objective optimization-based approach. On the other hand, the number of active receive antennas induces a tradeoff in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

With the recent advances in radio frequency (RF) energy harvesting (EH) technologies, wireless powered cooperative cognitive radio network (CCRN) has drawn an upsurge of interest for improving the spectrum utilization with incentive to motivate joint information and energy cooperation between the primary and secondary systems. Dedicated energy beamforming is aimed at remedying the low efficiency of wireless power transfer, which nevertheless arouses out-of-band EH phases and thus low cooperation efficiency. To address this issue, in this paper, we consider a novel CCRN aided by full-duplex (FD)-enabled energy access points (EAPs) that can cooperate to wireless charge the secondary transmitter while concurrently receiving primary transmitter's signal in the first transmission phase, and to perform decode-and-forward relaying in the second transmission phase. We investigate a weighted sum-rate maximization problem subject to transmitting power constraints as well as a total cost constraint using successive convex approximation techniques. A zero-forcing-based suboptimal scheme that requires only local channel state information for the EAPs to obtain their optimum receiving beamforming is also derived. Various tradeoffs between the weighted sum-rate and other system parameters are provided in numerical results to corroborate the effectiveness of the proposed solutions against the benchmark ones.

Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large-scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confidential messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics, random blockages, and antenna gains on the secrecy performance. For the special case of uniform linear array (ULA), a tractable approach is proposed to evaluate the average achievable secrecy rate. We also characterize the impact of artificial noise in such networks. Our results reveal that in the low transmit power regime, the use of low mmWave frequency achieves better secrecy performance, and when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining a higher secrecy rate. More antennas at the transmitting nodes are needed to decrease the antenna gain obtained by the eavesdroppers when using ULA. Eavesdroppers can intercept more information by using a wide beam pattern. Furthermore, the use of artificial noise may be ineffective for enhancing the secrecy rate.

This paper considers the massive multiple-input multiple-output (MIMO) enabled heterogeneous cloud radio access network (C-RAN), in which both remote radio heads (RRHs) and massive MIMO macrocell base stations (BS) are deployed to potentially accomplish high throughput and energy efficiency (EE). In this network, the soft fractional frequency reuse (S-FFR) is employed to mitigate the inter-tier interference. We develop a tractable analytical approach to evaluate the throughput and EE of the entire network, which can well predict the impacts of the key system parameters such as number of macrocell BS antennas, RRH density, and S-FFR factor, etc. Our results demonstrate that massive MIMO is still a powerful tool for improving the throughput of the heterogeneous C-RAN while RRHs are capable of achieving higher EE. The impact of S-FFR on the network throughput is dependent on the density of RRHs. Furthermore, more radio resources allocated to the RRHs can greatly improve the EE of the network.

Cognitive radio (CR) has been advocated to improve the network spectrum efficiency for decades, and the cooperation between the primary and secondary systems has become a new paradigm to further improve the spectrum utilization. However, in practice, secondary transmitters (STs) are usually power constrained, which limits the application of cooperative cognitive radio networks (CCRN). In this paper, to tackle this, we consider a novel spectrum sharing CCRN powered by energy access points (EAPs) that can charge users wirelessly, in which a multi-antenna secondary user (SU) solely powered by its harvested energy seeks cooperation with a single-antenna primary user (PU) by serving as a deocde-and-forward (DF) relay. We investigate a payoff maximization problem from the SU's perspective, who gets paid by offering data relaying service for PU but has to pay for WEH, and obtain its optimal DF relay and WEH strategy. A greedy-based algorithm that can assign the ST to right EAPs is also proposed for the ease of implementation. The proposed scheme is shown to be effective by simulations with a negligible gap to the optimal solution.

Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confidential messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in the mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics and large antenna arrays on the secrecy performance. We also characterize the impact of artificial noise in this networks. Our results reveal that in the low transmit power regime, the use of low mmWave frequency achieves better secrecy performance, when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining more secrecy rate. Eavesdroppers can intercept more information by using wide beam pattern. Furthermore, the use of artificial noise may be unable to enhance the secrecy rate for the case of low node density.

We investigate a pricing based approach to control interference from a tier-2 base station (BS) to a tier-1 BS in reversed time division duplex (TDD) multi-antenna systems. The tier-2 BS is being charged for causing interference to the tier-1 BS. Also, the tier-2 BS has to satisfy the signal-to-interference-plus-noise ratio (SINR) targets of its downlink users under a maximum transmission power constraint. Analytical and simulation studies are carried out to understand the behavior of the tier-2 BS for different charges and for different power budgets. Observations from the analyses suggest that the tier-1 BS can perform interference control and/or profit maximization without knowing the downlink channels of the tier-2 BS.

This paper considers the problem of maximizing the sum-rate for simultaneous wireless information and power transfer (SWIPT) in a full-duplex bi-directional communication system subject to energy harvesting and transmit power constraints at both nodes. We investigate the optimum design of the receive power splitters and transmit powers for SWIPT in full-duplex mode. Exploiting rate-split method, an iterative algorithm is derived to solve the non-convex problem. The effectiveness of the proposed algorithm is justified through numerical simulations.

The application of stochastic geometry theory for the study of cellular networks has gained huge popularity recently. Most existing works however rely on unrealistic assumptions concerning the underlying user traffic model. This paper aims to make a step in this direction by devising a new model for the performance analysis and optimization of heterogeneous cellular networks (HetNets) with irregular BS deployment and flow- level traffic dynamics. We provide a unified methodology for the evaluation of the flow rate with closed-form expressions of the useful signal power and aggregate network interference over Nakagami-m fading channels. The problem of computing the optimal loading factors which result in the greatest sustainable traffic whilst the system remains stable is formulated and tackled.

This paper considers a hybrid heterogeneous network (HetNet), where macro cells adopt massive multiple-input multiple-output (MIMO), and small cells adopt millimeter wave (mmWave) transmissions. We assume that all base stations (BSs) are solely powered by the renewable energy. The implementation of these emerging techniques has a substantial effect on the user association (UA). Motivated by this, we formulate a user association problem to maximize the network utility while the power cost of each BS does not exceed the harvested energy. To solve it, a low complexity distributed UA algorithm is proposed. The results demonstrate that the proposed algorithm achieves higher throughput than the max reference signal received power (RSRP) and max signal-to-interference-plus-noise ratio (SINR) UAs. It also shows that increasing the number of antennas at the macro cell BS with more power consumption, the throughput continues to increase by using the proposed algorithm, compared to the decrease in throughput by using the existing ones. Increasing the number of mmWave BSs, mmWave BS antennas or mmWave bandwidths can significantly improve the throughput. Compared with massive MIMO macro cells, mmWave small cells play a dominant role in enhancing the throughput of the networks due to the larger bandwidths.

This paper presents a stochastic geometry-based framework for the design and analysis of downlink multi-user multiple-input multiple-output (MIMO) heterogeneous cellular networks with linear zero-forcing transmit precoding and receive combining, assuming Rayleigh fading channels and perfect channel state information. The generalized tiers of base stations may differ in terms of their Poisson point process spatial density, number of transmit antennas, transmit power, artificial-biasing weight, and number of user equipments served per resource block. The spectral efficiency of a typical user equipped with multiple receive antennas is characterized using a non-direct moment-generating-function-based methodology with closed-form expressions of the useful received signal and aggregate network interference statistics systematically derived. In addition, the area spectral efficiency is formulated under different space-division multiple-access and single-user beamforming transmission schemes. We examine the impact of different cellular network deployments, propagation conditions, antenna configurations, and MIMO setups on the achievable performance through theoretical and simulation studies. Based on the state-of-the-art system parameters, the results highlight the inherent limitations of baseline single-input single-output transmission and conventional sparse macro-cell deployment, as well as the promising potential of multi-antenna communications and small-cell solution in interference-limited cellular environments.

This paper explores the potential of wireless power transfer (WPT) in massive multiple-input multiple-output (MIMO)-aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze user association schemes: 1) downlink received signal power (DRSP)-based approach for maximizing the harvested energy and 2) uplink received signal power (URSP)-based approach for minimizing the uplink path loss. We adopt the linear maximal-ratio transmission beamforming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then, we derive the average uplink achievable rate under the harvested energy constraint. Numerical results demonstrate that the use of massive MIMO antennas can improve both the users' harvested energy and uplink achievable rate in the HetNets; however, it has negligible effect on the ambient RF energy harvesting. Serving more users in the massive MIMO macrocells will deteriorate the uplink information transfer because of less harvested energy and more uplink interference. Moreover, although DRSP-based user association harvests more energy to provide larger uplink transmit power than the URSP-based one in the massive MIMO HetNets, URSP-based user association could achieve better performance in the uplink information transmission.

© 2015 IEEE. In this paper, we study the multiple-input multiple-output wiretap channel for simultaneous wireless information and power transfer, in which there is a base station (BS), an information-decoding (ID) user, and an energy-harvesting (EH) user. The messages intended to the ID user is required to be kept confidential to the EH user. Our objective is to design the optimal transmit covariance matrix at the BS for maximizing the ergodic secrecy rate subject to the harvested energy requirement for the EH user exploiting only statistical channel state information at the BS. To this end, we begin by deriving an approximation for the ergodic secrecy rate using large-dimensional random matrix theory and the method of Taylor series expansion. This approximation enables us to derive the asymptotic-optimal transmit covariance matrix that achieves the tradeoff for ergodic secrecy rate and harvested energy. The simulation results are provided to verify the accuracy of the approximation and show that a bigger rate-energy region can be achieved when the Rician factor increases or the path loss exponent decreases. We also show that when the transmit correlation increases or the distance between the eavesdropper and the BS decreases, the harvested energy will be increased, while the achieved ergodic secrecy rate decreases.

Interference alignment (IA) is known to achieve the degree-of-freedom (DoF) capacity of the interference channel, if full channel state information (CSI) is available at the transmitters perfectly. Challenges, however, arise when CSI is not perfect, and the achievable capacity of IA is not well understood. In this paper, we study the achievable performance of the interference channel using perfect IA techniques based on imperfect CSI. In particular, we obtain the statistical distribution of the maximum achievable rate per stream of the channel. Utilizing our analytical results, we derive new nonasymptotic performance metrics that are then used to 1) optimize the number of streams per user for maximizing the network sum-rate and 2) assess the performance of IA in the time-varying block fading channel. Numerical results are provided to reveal the accuracy of our analytical results.

This paper studies secrecy transmission with the aid of a group of wireless energy harvesting-enabled amplify-and-forward (AF) relays performing cooperative jamming (CJ) and relaying. The source node in the network does simultaneous wireless information and power transfer with each relay employing a power splitting receiver in the first phase; each relay further divides its harvested power for forwarding the received signal and generating artificial noise for jamming the eavesdroppers in the second transmission phase. In the centralized case with global channel state information (CSI), we provide the closed-form expressions for the optimal and/or suboptimal AF-relay beamforming vectors to maximize the achievable secrecy rate subject to individual power constraints of the relays, using the technique of semidefinite relaxation (SDR), which is proved to be tight. A fully distributed algorithm utilizing only local CSI at each relay is also proposed as a performance benchmark. Simulation results validate the effectiveness of the proposed multi-AF relaying with CJ over other suboptimal designs.

This paper exploits the potential of massive multiple input multiple output (MIMO) in K-tier heterogeneous cellular networks (HCNs), to enhance the data rate for 5G. In such a network, macro base stations (MBSs) are equipped with large number of antennas and support multi-user transmission. We first examine the impact of massive MIMO on user association in K-tier HCNs. Exact and asymptotic expressions for the probability of a user being associated with a macro cell or a small cell are derived. Based on the asymptotic analysis, the impacts of system parameters such as tier's density and BS transmit power on user association are explicitly identified. Furthermore, we derive the coverage probability and rate of the proposed network. Numerical results corroborate our analysis and show that the implementation of massive MIMO in macro cells can significantly enhance the performance of HCNs in terms of coverage and rate. A guideline for practical cellular deployment is reached that MBSs with large antenna arrays can decrease the demands for small cells.

Compared to the existing lower frequency wireless power transfer, millimeter wave (mmWave) power transfer takes advantage of the high-dimensional multi-antenna and narrow beam transmission. In this paper we introduce wireless power transfer for mmWave cellular networks. Here, we consider users with large energy storage that are recharged by the mmWave base stations prior to uplink information transmission, and analyze the average harvested energy and average achievable rate. Numerical results corroborate our analysis and show that the serving base station plays a dominant role in wireless power transfer, and the contribution of the interference power from the interfering base stations is negligible, even when the interfering base stations are dense. By examining the average achievable rate in the uplink, when increasing the base station density, a transition from a noise-limited regime to an interference-limited regime is observed.

This paper considers simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output (MISO) downlink systems in which a multi-antenna transmitter sends a secret message to a single-antenna information receiver (IR) and power to multiple single-antenna energy receivers (ERs) in presence of external eavesdroppers. We aim to maximize the harvested energy by the ERs while maintaining the signal-to-interference and noise ratio (SINR) threshold at the IR and keeping the message secure from possible eavesdropping by the malicious attackers by interfering their reception. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are studied. Using semidefinite relaxation (SDR) techniques, we show that a rank-one optimal beamforming solution can always be constructed for the IR.

In this paper, we consider the uplink of a dense small cell network (SCN) using pilot reuse in channel training and maximum ratio combining (MRC) for data detection. Taking into account imperfect channel state information (CSI) caused by pilot contamination, we derive exact closed-form expressions of the per-user achievable ergodic rate with arbitrary pilot reuse factors. After that, we first reveal that the user terminals, which are geographically separated with large distance between each other, can reuse pilot, suffering only low pilot contamination. Based on this insight, we further propose a low-complexity pilot reuse algorithm based on the minimum sum of estimation error criterion. Simulation results verify our theoretical analysis and demonstrate that the proposed pilot reuse algorithm is very effective in suppressing pilot contamination in SCN.

This paper investigates the antenna selection scheme choosing the antenna with the largest channel gain to the the cellular user for device-to-device (D2D) communication underlaying cellular networks. We derive an exact closed-form expression of the ergodic achievable rate and examine its asymptotic behavior in the high signal-to-noise ratio (SNR) regime. It is demonstrated that the high SNR approximation can be much improved by a higher transmit power ratio between the base station (BS) and the D2D transmitter in the small cell setting where all users are closely located. However, in the macro cell setting in which only the D2D terminals are fairly close, the influence of the transmit power ratio becomes insignificant. In addition, we present upper and lower bounds of the ergodic achievable rate. Based on these results, we illustrate that the D2D communication cannot help in the cellular network for elevating the rate when the transmit SNR at the BS grows high. If the BS SNR is lower, then the D2D communication can effectively increase the ergodic achievable rate. Numerical results are provided to justify the correctness of the expressions and the relevant performance analysis.

This paper gives a replica analysis for the minimum mean square error (MSE) of a massive multiple-input multipleoutput (MIMO) system by using Bayesian inference. The Bayesoptimal estimator is adopted to estimate the data symbols and the channels from a block of received signals in the spatial-temporal domain. We show that using the Bayes-optimal estimator, the interfering signals from adjacent cells can be separated from the received signals without pilot information of the interfering signals. In addition, the MSEs with respect to the data symbols and the channels of the desired users decrease with the number of receive antennas and the number of data symbols, respectively. There are no residual interference terms that remain bounded away from zero as the numbers of receive antennas and data symbols approach infinity.

Simultaneous wireless information and power transfer (SWIPT) has recently drawn much attention for its dual use of radio signals. A new type of relays, wireless energy harvesting (WEH)-enabled relays, are thus motivated to support cooperation. In this paper, we consider the use of power splitter (PS) at such relays for a distributed WEH-enabled amplify-and-forward (AF) relaying network, where a multi-antenna transmitter communicates with a single-antenna receiver with the aid of several single-antenna WEH-enabled AF relays, in the presence of a single-antenna eavesdropper. Assuming global channel state information (CSI) at the transmitter but local CSI from/to legitimate parties at the relays, the secrecy rate maximization problem is studied to optimize the beamforming vector at the transmitter as well as the PS ratios and the AF coefficients at the relays. We devise an efficient secure relay beamforming (SRB) algorithm to first obtain the PS ratios in a distributed manner and then iteratively adapt the transmit beam and the AF coefficients. The efficiency of the proposed algorithm is evaluated against other heuristic schemes by simulations.

The use of low precision (e.g., 1 - 3 bits) analog-to-digital converters (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be very large just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the MIMO system with low precision ADCs is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the MIMO system.

This study investigates the outage probability of device‐to‐device communication assisted by a relay node utilising a one‐way amplify‐and‐forward relaying strategy. The authors assume that all the terminals are equipped with a single antenna and all the users know perfect channel state information. They first derive the exact closed‐form expression for characterising the outage probability performance of the system. They subsequently discuss several special scenarios and obtain the asymptotic results for each of the considered scenarios. The results can be easily computed with only the channel statistics. Based on the analysis in the high signal‐to‐noise ratio regime, closed‐form power allocation policies are developed to improve the outage probability performance. The author's analytical results are validated via Monte Carlo computer simulations.

This study investigates the scheduling problem for a single‐cell downlink distributed antenna systems (DASs) with a massive number of remote access units (RAUs). To reduce signalling overhead under limited backhaul capacity, the authors make use of local long‐term channel state information (CSI) in coordinated scheduling design. They first derive the ergodic rate expressions for both the single RAU transmission and the cooperative RAU transmission modes as functions of long‐term CSI. Then greedy scheduling algorithms (GSAs) aiming for the maximum ergodic sum rate for the massive DAS using long‐term CSI are proposed. To mitigate the intra‐cell interference, a two‐stage GSA with hybrid transmission mode is devised. Asymptotic analysis reveals that as the number of RAUs goes to infinity, intra‐cell interference can be effectively mitigated. Simulation results verify the analysis and demonstrate that the two‐stage GSA exhibits a higher ergodic sum‐rate.

This paper presents a resource allocation framework for device-to-device (D2D) communications underlaying uplink MIMO cellular networks. At first, our aim is to address the sum-rate maximization problem of the cellular network with both D2D and cellular users. An algorithm based on pure random search is presented for obtaining the optimal resource allocation without using an exhaustive search. Then, we propose a noncooperative resource allocation game for the joint self-optimization of channel allocation, power control, and precoding of the D2D users in a more practical setting. The feasibility and existence of the pure strategy Nash equilibrium are then established. An iterative algorithm based on best response dynamic is then proposed to determine the feasible pure strategy Nash equilibrium under specific conditions. As the algorithm may not always converge, we devise a strategy refinement mechanism to tackle this issue based on the sum-rate criterion. Simulation results verify our theoretical analysis and findings.

This paper considers a multiple-input single-output downlink system consisting of one multiantenna transmitter, one single-antenna information receiver (IR), and multiple single-antenna energy-harvesting receivers (ERs) for simultaneous wireless information and power transfer. The design is to keep the message secret to the ERs while maximizing the information rate at the IR and meeting the energy harvesting constraints at the ERs. Technically, our objective is to optimize the information-bearing beam and artificial noise energy beam for maximizing the secrecy rate of the IR subject to individual harvested energy constraints of the ERs for the case where the ERs can collude to perform joint decoding in an attempt to illicitly decode the secret message to the IR. As a by-product, we also solve the total power minimization problem subject to secrecy rate and energy harvesting constraints. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are addressed. For the imperfect CSI case, we study both eavesdroppers' channel covariance-based and worst case-based designs. Using semidefinite relaxation (SDR) techniques, we show that there always exists a rank-one optimal transmit covariance solution for the IR. Furthermore, if the SDR results in a higher rank solution, we propose an efficient algorithm to always construct an equivalent rank-one optimal solution. Computer simulations are carried out to demonstrate the performance of the proposed algorithms.

In this paper, we investigate the secrecy performance for a multiple-input multiple-output (MIMO) wiretap channel in the presence of a multiantenna eavesdropper. In particular, the legitimate transmitter uses transmit antenna selection (TAS) to transmit on a single antenna with the largest signal-to-noise ratio (SNR) while both the legitimate receiver and the eavesdropper adopt maximal ratio combining (MRC) for reception. We derive exact closed-form expressions for the probabilities of achieving positive secrecy rate and secrecy outage in the case of imperfect feedback due to feedback delay and/or feedback error. Furthermore, we derive the asymptotic secrecy outage probability at high SNR, which accurately reveals the secrecy diversity loss due to imperfect feedback. Simulation results are provided to verify our analytical results and illustrate the impact of imperfect feedback on the secrecy performance of such a wiretap system.

This paper considers a multipair massive multiple-input-multiple-output two-way relay network, in which multiple pairs of users are served by a relay station with a large number of antennas, which uses maximum ratio combining/maximum ratio transmission and a fixed amplification factor for reception/ transmission. First, the users' ergodic rates are derived for the case with a finite number of antennas, and then, the rate gain is analyzed when the transmit power of the senders and the relay is sufficiently large. We show that the ergodic rates increase with the number of antennas at the relay, i.e., N, but decrease with the number of user pairs, i.e., K, both logarithmically. The energy efficiency for the network is also investigated when the number of antennas grows to infinity. It is further revealed that the ergodic sum-rate can be maintained while the users' transmit power is scaled down by a factor of 1/N or the relay power by a factor of 2K/N. This indicates that users obtain an energy efficiency gain of N, but the relay has an energy efficiency gain of N divided by the number of users, i.e., 2K.

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen's inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SE-SDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the impact of the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician $K$-factor, on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician $K$-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zero-forcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician $K$-factor regime.

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.

Copyright © 2013 John Wiley & Sons, Ltd. To utilize spectrum resources more efficiently, dynamic spectrum access attempts to allocate the spectrum to users in an intelligent manner. Uncoordinated sharing with cognitive radio (CR) users is a promising approach for dynamic spectrum access. In the uncoordinated sharing model, CR is an enabling technology that allows the unlicensed or secondary users to opportunistically access the licensed spectrum bands (belonging to the so-called primary users), without any modifications or updates for the licensed systems. However, because of the limited resources for making spectrum observations, spectrum sensing for CR is bound to have errors and will degrade the grade-of-service performance of both primary and secondary users. In this paper, we first propose a new partial spectrum sharing policy, which achieves efficient spectrum sharing between two licensed networks. Then, a Markov chain model is devised to analyze the proposed policy considering the effects of sensing errors. We also construct a cross-layer design framework, in which the parameters of spectrum sharing policy at the multiple-access control layer and the spectrum sensing parameters at the physical layer are simultaneously coordinated to maximize the overall throughput of the networks, while satisfying the grade-of-service constraints of the users. Numerical results show that the proposed spectrum sharing policy and the cross-layer design strategy achieve a much higher overall throughput for the two networks.

By deploying a substantial number of antennas on the base station (BS) side, massive MIMO system has been demonstrated to achieve unprecedented spectral efficiency. However, its performance is still limited by pilot contamination due to unavoidable reuse of pilot sequences from user terminals in other cells. In this paper, we focus on the performance analysis of zero-forcing beamforming in a finite-antenna massive MIMO system using a time-shifted pilot scheme, which was shown to combat pilot contamination effectively with infinite BS antennas and conjugate beamforming. We derive rigorous expressions for sum-rate lower bounds and associated signal to interference plus noise ratios of both forward link and reverse link. Based on these expressions, we provide some engineering rules of thumb to design a time-shifted pilot system achieving higher sum-rate performance. Besides, our system model is proved to cover a series of previous works as special cases as well.

A multi-pair two-way relay system sharing with a single relay with large numbers of antennas is considered. In this paper, we investigate the ergodic achievable rates when maximum ratio combining/maximum ratio transmission is used at the relay station. The analytical results for the achievable rates are derived and the asymptotic analysis is conducted when the number of antennas, N, grows to infinity. Results demonstrate a number of phenomena including the logarithmic increase of the average rates with the number of antennas, and the logarithmic decrease of the average rates with the number of interference pairs, K-1, when the transmit power of both relay and users are fixed.

This paper studies the outage probability of device-to-device (D2D) communication aided by another D2D user using the two-way amplify-and-forward (AF) relaying protocol. We first discuss the outage behavior under strong and weak interference from the cellular network. Then the exact expressions for the outage probability under the two cases are derived. Based on these results, we give tight approximations in the high signal-to-noise (SNR) regime under the two cases. Numerical results show that the outage behavior for the relay aided D2D link can be greatly enhanced without extra power. Analytical results are validated via comparisons with the Monte-Carlo simulations.

This study considers communication from a source to a destination with the aid of a set of cooperative relaying nodes. Unlike previous studies in energy efficiency, the authors studied the effect of relay placements together with different relay‐selection timing on the performance. The cooperative relaying schemes for a general relay placement and some specialised relay placements are characterised and analysed by a Markov chain model. They derive the expressions for the throughput and the expected energy consumption for both proactive and reactive relay selection for different relay placements and densities. By using the analytical expressions, the authors find the optimal relay locations for different relay‐selection schemes to achieve higher energy efficiency with the consideration of system throughput. The performance improvements offered by the authors proposed relay placement are demonstrated by numerical results. Moreover, the two new cooperative relaying schemes with selection combining for a certain relay placement are discussed. Their throughput and energy consumption are also derived and compared with the existing techniques.

Jamming in defence applications is increasingly difficult because of advanced signal processing countermeasures. In this study, task‐dependent power‐constraint optimal jamming techniques are investigated. To prevent the target from being detected, a novel jamming technique is proposed to minimise the signal‐to‐interference‐plus‐noise ratio (SINR) of the radar for extended known and stochastic target. To impair the parameter estimation performance, another jamming technique is proposed which minimises the mutual information (MI) between the radar return and the stochastic target impulse response. The optimal jamming spectrum is obtained assuming that the jammer has intercepted the radar waveform generally. However, the precise characteristic of radar waveform is impossible to capture in practice. To model this, it is considered that the waveform spectrum lies in an uncertainty class confined by known upper and lower bounds. Then, the minimax robust jamming is designed based on the SINR and MI criteria, which optimises the worst‐case performance. Results demonstrate that the two criteria lead to different optimal jamming results but they have a close relationship from the Shannon's capacity equation which provides useful guidance on jamming power allocation for different jamming tasks. However, their behaviour with respect to the waveform uncertainty is the same.

Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding. However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communication is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.

This paper presents an analytical investigation on the performance of Rayleigh-product MIMO channels with linear minimum mean-square-error (MMSE) or zero-forcing (ZF) receivers. For MMSE receivers, exact closed-form expressions for the ergodic sum-rate of the system are derived. In addition, simplified expressions are obtained for the key parameters dictating the sum-rate performance of the system in the high signal-to-noise ratio (SNR) regime (i.e., high SNR slope and power offset) and low SNR regime (i.e., minimum energy per information bit required to convey any positive rate and the wideband slope). While for ZF receivers, tight closed-form upper and lower bounds for the ergodic sum-rate of the system are derived. It is analytically proven that the ZF and MMSE receivers achieve the same sum rate performance in the high SNR regime. Moreover, for both MMSE and ZF receivers, the achievable diversity-multiplexing tradeoff (DMT) of Rayleigh-product MIMO channels is characterized. The findings suggest that a larger number of scatterers will improve the the performance of Rayleigh-product MIMO channels with linear receivers, and the ZF receivers achieve the same performance as the MMSE receivers in Rayleigh-product MIMO channels in the high SNR regime. Moreover, it is demonstrated that as long as the number of the scatterers is greater than the number of receive antennas, linear receivers achieve the optimal DMT.

This paper investigates the scheduling problem for a single-cell distributed multiple-input multiple-output (d-MIMO) downlink system with a massive number of remote access units (RAUs), N. We first derive the ergodic rate expressions for both the single RAU transmission (SRT) and the cooperative RAU transmission (CRT) modes as functions of long-term channel state information (CSI). Then, greedy scheduling algorithms aiming for maximizing the ergodic sum rate for the massive d-MIMO system using local long-term CSI are proposed. To mitigate intra-cell interference, a two-stage greedy scheduling algorithm (GSA) is developed to further improve the ergodic sum rate. Asymptotic analysis reveals that with infinite N intra-cell interference can be efficiently mitigated. Simulation results verify the derived expressions and demonstrate that the two-stage GSA exhibits a higher ergodic sum rate.

In this paper, we study the achievable uplink rates of massive multiple-input multiple-output (MIMO) systems using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming imperfect channel state information (CSI). Unlike all previous studies, the fast fading MIMO channel matrix here is modeled to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. In particular, it is found that with a non-zero Ricean K-factor, the approximations and the exact uplink rates converge to the same constant value if the number of base station antennas, M, grows large, while the transmit power of each user is scaled down proportionally to 1/M. However, if the channel is Rayleigh fading, we can only cut the transmit power of each user proportionally to 1/√M. In addition, we show that with increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

Channel estimation attacks can degrade the performance of the legitimate system and facilitate eavesdropping. It is known that pilot contamination can alter the legitimate transmit precoder design and strengthen the quality of the received signal at the eavesdropper, without being detected. In this paper, we devise a technique which employs random pilots chosen from a known set of phase-shift keying (PSK) symbols to detect pilot contamination. The scheme only requires two training periods without any prior channel knowledge. Our analysis demonstrates that using the proposed technique in a massive MIMO system, the detection probability of pilot contamination attacks can be made arbitrarily close to 1. Simulation results reveal that the proposed technique can significantly increase the detection probability and is robust to noise power as well as the eavesdropper's power.

In this study, the authors investigate the error performance of the protograph (low‐density parity check) codes over Nakagami‐ m fading relay channels. The authors first calculate the decoding thresholds of the protograph codes over such channels with different fading depths (i.e. different values of m ) by exploiting the modified protograph extrinsic information transfer (PEXIT) algorithm. Furthermore, based on the PEXIT analysis and using Gaussian approximation, the authors derive the bit‐error‐rate (BER) expressions for the error‐free (EF) relaying protocol and decode‐and‐forward (DF) relaying protocol. The authors finally compare the threshold with the theoretical BER and the simulated BER results of the protograph codes. It reveals that the performance of DF protocol is approximately the same as that of EF protocol. Moreover, the theoretical BER expressions, which are shown to be reasonably consistent with the decoding thresholds and the simulated BERs, are able to evaluate the system performance and predict the decoding threshold with lower complexity as compared with the modified PEXIT algorithm. As a result, this work can facilitate the design of the protograph codes for the wireless communication systems.

In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.

The articles in this special issue focus on the technology and applications supported by virtual multiple antennas, or VMIMOs. The impetus for this has been spurred by the strong desire to understand VMIMO, which is a rapidly growing research area. VMIMO is believed to be a key technology for beyond 4th generation mobile communications technologies (B4G). It enables one to make use of all the neighboring terminals and amortize the cost of multiple antennas; hence, a large MIMO channel can be created to increase capacity significantly as well as improve error rate performance. Nevertheless, fundamental roadblocks need to be addressed in order to take full advantage of VMIMO.

In this paper, we investigate the outage performance for a decode-and-forward two-way relay network in the presence of multiple strong interferers at the source/destination terminals. We first derive closed-form expressions for the outage probability of the system under asymmetrical and symmetrical cases, of whether the received powers at the relay from both source terminals are the same or different. Based on the analytical expressions, we perform asymptotic analysis in the case where the power of relay or/and the power of terminals become infinite. Our analysis shows that the outage probability is lower when there is one dominating interferer than when there are several equal-power interferers. In addition, optimal power allocation between the two source terminals and the diversity order of the system are investigated in asymptotic analysis. Simulation results demonstrate that our analytical results are in excellent agreement with the Monte Carlo simulations.

The secrecy capacity region for theK-receiver degraded broadcast channel (BC) is given for confidential messages sent to the receivers and to be kept secret from an external wiretapper. Superposition coding and Wyner's random code partitioning are used to show the achievable rate tuples. Error probability analysis and equivocation calculation are also provided. In the converse proof, a new definition for the auxiliary random variables is used, which is different from either the case of the two-receiver BC without common message or theK-receiver BC with common message, both with an external wiretapper, or theK-receiver BC without a wiretapper.

Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general type of fading distributions have to be included. The mutual information expression can be expressed as functionals of the Stieltjes transform through the so-called Shannon transform. Ideally, if the Stieltjes transform is known in the context of the large-dimensional RMT, then the problem is solved. However, it is difficult to derive the Stieltjes transform of the considered channel models directly, especially when the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. To overcome this, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large-dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels. As an application, we determine the capacity-achieving input covariance matrices for the MIMO-MACs and prove that the capacity-achieving input covariance matrices are asymptotically independent of the fading distribution.

In this paper, we analyze the ergodic sum-rate of a multi-cell downlink system with base station (BS) cooperation using regularized zero-forcing (RZF) precoding. Our model assumes that the channels between BSs and users have independent spatial correlations and imperfect channel state information at the transmitter (CSIT) is available. Our derivations are based on large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the BS and users approach to infinity with some fixed ratios. In particular, a deterministic equivalent expression of the ergodic sum-rate is obtained and is instrumental in getting insight about the joint operations of BSs, which leads to an efficient method to find the asymptotic-optimal regularization parameter for the RZF. In another application, we use the deterministic channel rate to study the optimal feedback bit allocation among the BSs for maximizing the ergodic sum-rate, subject to a total number of feedback bits constraint. By inspecting the properties of the allocation, we further propose a scheme to greatly reduce the search space for optimization. Simulation results demonstrate that the ergodic sum-rates achievable by a subspace search provides comparable results to those by an exhaustive search under various typical settings.

ABSTRACT We develop an efficient hard detector for multiple‐input multiple‐output (MIMO) channels, which adaptively combines maximum‐likelihood detection (MLD) and minimum‐mean‐square error with a successive interference canceler together. Unlike the conventional joint combination scheme, which may suffer from considerable degradation in bit‐error‐rate (BER) performance over correlated channels and where only one data stream is detected by MLD, our proposed scheme adaptively controls the number of data streams to be detected by MLD based on an analytical characterization of reliability for the detection. Simulation results illustrate that near‐optimal BER performance can be obtained at much lower computational complexity by the proposed method as compared with existing techniques, regardless of the spatial correlation of the MIMO channels. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT In this paper, we propose a heterogeneous‐prioritized spectrum sharing policy for coordinated dynamic spectrum access networks, where a centralized spectrum manager coordinates the access of primary users (PUs) and secondary users (SUs) to the spectrum. Through modeling the access of PUs and multiple classes of SUs as continuous‐time Markov chains, we analyze the overall system performance with consideration of a grade‐of‐service guarantee for both the PUs and the SUs. In addition, two new call admission control (CAC) strategies are devised in our models to enhance the maximum admitted traffic of SUs for the system. Numerical results show that the proposed heterogeneous‐prioritized policy achieves higher maximum admitted traffic for SUs. The trade‐off between the system's serving capability and the fairness among multiple classes of SUs is also studied. Moreover, the proposed CAC strategies can achieve better performance under max‐sum, proportional, and max‐min fairness criteria than the conventional CAC strategies. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, the outage performance for a decode-and-forward two-way relay network is investigated in the presence of multiple interferers at the source terminals. The exact expression for the outage probability is derived and the disparity is discussed between symmetrical and asymmetrical cases. Based on the closed-form expressions the optimal power allocation between source terminals is discussed and the diversity order is derived. The effect of interference power is also studied. Simulation results are provided to validate the analytical results.

This paper proposes a statistical-eigenmode spacedivision multiple-access (SE-SDMA) transmission for a two-user downlink system where two transmit antennas are equipped at the base station and each mobile user has one receive antenna, assuming that only statistical channel state information (CSI) is available at the transmitter. By maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio, the proposed SE-SDMA approach selects two users with orthogonal principal statistical eigen-directions and transmits to each user along the corresponding eigenmode. We derive an exact expression of the ergodic achievable rate, and compare it with the zero-forcing beamforming (ZFBF) system exploiting instantaneous CSI. It is shown that SE-SDMA can achieve the maximum ergodic sum-rate of the two selected users, and provide significant user selection gain. Analytical and simulation results show that the rate gap between SE-SDMA and ZFBF with perfect CSI at the transmitter can tend to zero in highly correlated channels, which indicates that statistical precoding can be used instead of instantaneous precoding in certain environments.

In this paper, we investigate the asymptotic mutual information expression and the capacity-achieving input covariance matrices for the coordinated multi-point multiple-input multiple-output (MIMO) antenna channels. In particular, it is considered that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our derivations are based on novel techniques from large dimensional random matrix theory. In contrast to previous studies, we consider a general model in which the correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian distributed. We show that the asymptotic capacity is invariant to all types of fading distribution. As such, the asymptotic mutual information expression is robust and has wide applicability.

In this paper, we focus on a general coordinated multi-point (CoMP) multiple input multiple-output (MIMO) system consisting of multiple users and multiple base stations (BSs) equipped with multiple antennas, respectively. An asymptotic ergodic mutual information expression and the capacity-achieving input covariance matrices for the system are derived employing novel techniques from large dimensional random matrix theory (RMT). The asymptotic regime is based on the assumption that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our contributions are to extend the previous results to the general channel model with two-sided spatial correlation and line-of-sight (LOS), in which the transmit and receive correlation matrices are both generally nonnegative definite and the channel entries are non-Gaussian distributed. Simulations show that the asymptotic capacity is accurate even for finite number of antenna and invariant to all types of fading distribution.

In this paper, we study adaptive transmission mode switching between statistical and instantaneous channel state information (CSI) aided single-user (SU) and multiuser (MU) precoding for a two-user downlink system, where two transmit antennas are equipped at the base station and each mobile user has one receive antenna. In the case where only statistical CSI (SCSI) is available at the transmitter, a statistical-eigenmode space-division multiple-access (SE-SDMA) scheme is proposed by maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio. An exact analytical expression of the ergodic achievable rate is derived for the proposed SE-SDMA and compared with SU schemes such as SE transmission (SET) and instantaneous CSI (ICSI)-aided beamforming (BF), as well as the MU schemes such as ICSI-aided zero-forcing BF (ZFBF). Assuming the ICSI obtained at the transmitter is imperfect, the operating regions of these schemes are determined for different signal-to-noise ratio regions, channel correlation levels and ICSI inaccuracy levels.

In wireless communications, spatial diversity techniques, such as space-time block code and single-input multiple-output (SIMO), are employed to strengthen the robustness of the transmitted signal against channel fading. This article studies the performance of protograph-based low-density parity-check (LDPC) codes with receive antenna diversity. The authors first propose a modified version of the protograph extrinsic information transfer algorithm and use it for deriving the threshold of the protograph codes in a SIMO system. The authors then calculate the decoding threshold and simulate the bit-error rate (BER) of two protograph codes (accumulate-repeat-by-3-accumulate (AR3A) code and accumulate-repeat-by-4-jagged-accumulate (AR4JA) code), a regular (3,6) LDPC code and two optimised irregular LDPC codes. The results reveal that the irregular codes achieve the best error performance in the low signal-to-noise-ratio (SNR) region and the AR3A code outperforms all other codes in the high-SNR region. Utilising the theoretical analyses and the simulated results, the authors further discuss the effect of the diversity order on the performance of the protograph codes. Accordingly, the AR3A code stands out as a good candidate for wireless communication systems with multiple receive antennas.

This study considers a scenario where two licensed networks share spectrum partially and opportunistically based on sensing of cognitive radios. The authors' main contribution is a discrete-time Markov chain analysis characterising the impact of imperfect sensing and sensing periodicity on the network performance. A cross-layer design framework is also developed, where the parameters in spectrum sensing and spectrum sharing are jointly optimised. Results show that the proposed policy achieves a higher throughput than conventional strategies.

In this study, the authors derive the effective capacity limits for multiple antenna channels which quantify the maximum achievable rate with consideration of link-layer delay-bound violation probability. Both correlated multiple-input single-output and multiple-input multiple-output keyhole channels are studied. Based on the closed-form exact expressions for the effective capacity of both channels, the authors look into the asymptotic high and low signal-to-noise ratio regimes, and derive simple expressions to gain more insights. The impact of spatial correlation on effective capacity is also characterised with the aid of a majorisation theory result. It is revealed that antenna correlation reduces the effective capacity of the channels and a stringent quality-of-service requirement causes a severe reduction in the effective capacity but can be alleviated by increasing the number of antennas.

In this study, the performances of selection cooperation are investigated in a scenario based on decode-and-forward and where equal gain combining (EGC) technique is adopted at the destination. Assuming that the channels suffer from independent non-identical Rayleigh fading, we first derive the cumulative distribution function, probability density function and moment generating function for the total instantaneous signal-to-noise ratio (SNR) at the destination after EGC. Then, these statistical functions are used to derive closed-form expressions for average SNR output, outage probability and average symbol error rate (SER) of selection cooperation. The results hold for arbitrary number of relays and refer to multiple-phase shift keying (M-PSK) modulations. Finally, simulations are carried out to verify the correctness of our theoretical analysis. A random network model is introduced to investigate the effect of relay number and path loss exponent on outage probability and average SER. In addition, based on this random network, the comparison between the performance of selection cooperation with EGC and that of selection cooperation with maximal ratio combining are performed.

This paper investigates the performance of optimal single stream beamforming schemes in multiple-input multiple-output (MIMO) dual-hop amplify-and-forward (AF) systems. Assuming channel state information is not available at the source and relay, the optimal transmit and receive beamforming vectors are computed at the destination, and the transmit beamforming vector is sent to the transmitter via a dedicated feedback link. Then, a set of new closed-form expressions for the statistical properties of the maximum eigenvalue of the resultant channel is derived, i.e., the cumulative density function (cdf), probability density function (pdf) and general moments, as well as the first order asymptotic expansion and asymptotic large dimension approximations. These analytical expressions are then applied to study three important performance metrics of the system, i.e., outage probability, average symbol error rate and ergodic capacity. In addition, more detailed treatments are provided for some important special cases, e.g., when the number of antennas at one of the nodes is one or large, simple and insightful expressions for the key parameters such as diversity order and array gain of the system are derived. With the analytical results, the joint impact of source, relay and destination antenna numbers on the system performance is addressed, and the performance of optimal beamforming schemes and orthogonal space-time block-coding (OSTBC) schemes are compared. Results reveal that the number of antennas at the relay has a great impact on how the numbers of antennas at the source and destination contribute to the system performance, and optimal beamforming not only achieves the same maximum diversity order as OSTBC, but also provides significant power gains over OSTBC.

This paper addresses coordinated downlink beamforming optimization in multicell time division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.

This letter investigates masked beamforming schemes for multiuser multiple-input multiple-output (MIMO) downlink systems in the presence of an eavesdropper. With noisy and outdated channel state information (CSI) at the base station (BS), we aim to maximize the transmit power of an artificial noise, which is broadcast to jam any potential eavesdropper, while meeting individual minimum mean square error (MMSE) constraints of the desired user links. To this end, we adopt a Bayesian approach and derive an average MSE uplink-downlink duality with imperfect CSI. Using the duality, a robust beamforming algorithm is proposed. Simulation results show the effectiveness of the proposed scheme.

In this paper, the outage performance for amplify-and-forward two-way relay channels is studied in the presence of co-channel interference. We derive the exact outage probability by integral-form expression, and approximate the outage probability with closed-form expression. It is shown by numerical results that the approximations fit well with the exact results in all signal-to-interference plus noise ratio regions, and the approximation perform more exactly when the sum power of interference being large much than the power of noise. Also, the outage performance for different interferers' power distributions are compared in simulations and it is shown the distribution of interferers' power has little effect to the outage probability of system.

In this paper, we study the capacity-achieving input covariance matrices for the jointly-correlated (or the Weichsel-berger) Rician fading multiple-input multiple-output (MIMO) antenna channel when perfect channel state information (CSI) is known at the receiver while only statistical CSI at the transmitter is available. Unlike the Kronecker model, such jointly-correlated MIMO channel accounts for the correlation coupled between the two ends and has been shown to be most accurate for representing real channels. Our contribution includes the expression for the asymptotic mutual information for the jointly-correlated Rician fading MIMO channel in the large-system regime in which the numbers of antennas at the transmitter and receiver go to infinity with a fixed ratio. Based on this expression, an efficient algorithm is also proposed to obtain the capacity-achieving input covariance matrix. Simulation results demonstrate that even for a moderate number of antennas at each end, the proposed scheme provides undistinguishable results as those obtained by the highly-complex stochastic programming (or Monte-Carlo based) approach.

This paper presents an analytical performance investigation of the capacity limits of correlated multiple-input single-output (MISO) channels in the presence of quality-of-service (QoS) requirements. Exact closed-form expression for the effective capacity of correlated MISO channels is derived. In addition, simple expressions are obtained at the asymptotic high and low signal-to-noise ratio (SNR) regimes, which provide insights into the impact of various system parameters on the effective capacity of the system. Also, a complete characterization of the impact of spatial correlation on the effective capacity is provided with the aid of a majorization theory result. The findings suggest that antenna correlation reduce the effective capacity of the channels. Moreover, a stringent QoS requirement causes a significant reduction in the effective capacity but this can be effectively alleviated by increasing the number of antennas.