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.

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

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

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.

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

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.

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air computation (AirComp) as a transformative paradigm for edge intelligence. To enhance the efficiency and scalability of AirComp systems, this paper proposes a comprehensive dual-approach framework that systematically transitions from traditional mathematical optimization to deep reinforcement learning (DRL) for resource allocation under execution uncertainty. Specifically, we establish a rigorous system model capturing execution uncertainty via Gamma-distributed computational workloads, resulting in challenging nonlinear optimization problems involving complex Gamma functions. For single-user scenarios, we design advanced block coordinate descent (BCD) and majorization-maximization (MM) algorithms, which yield semi-closed-form solutions with provable performance guarantees. However, conventional optimization approaches become computationally intractable in dynamic multi-user environments due to inter-user interference and resource contention. To this end, we introduce a Deep Q-Network (DQN)-based DRL framework capable of adaptively learning optimal policies through environment interaction. Our dual methodology effectively bridges analytical tractability with adaptive intelligence, leveraging optimization for foundational insight and learning for real-time adaptability. Extensive numerical results corroborate the performance gains achieved via increased edge server density and validate the superiority of our optimization-to-learning paradigm in next-generation AirComp systems.

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

Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

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

The deployment of extremely large antenna arrays (ELAAs) in extremely large-scale multiple-input multiple-output (XL-MIMO) systems introduces significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. When combined with the dual-wideband effects inherent to wideband systems, these phenomena fundamentally alter the channel’s sparsity patterns in the angular-delay domain, rendering existing estimation methods insufficient. To address these challenges, this paper reconsiders the channel estimation problem for wideband XL-MIMO systems. Leveraging the spatial-chirp property of array responses, we first quantitatively characterize the angular-delay domain sparsity of wideband XL-MIMO channels, revealing both global block sparsity and local common-delay sparsity. To effectively capture this structured sparsity, we then propose a novel column-wise hierarchical prior model that integrates a precision sharing mechanism and a Markov random field (MRF) structure. Building on this prior model, the channel estimation task is formulated as a multiple measurement vector (MMV)-based Bayesian inference problem. Tailored to the complex factor graph induced by this hierarchical prior, we develop a MMV-based hybrid message passing (MMV-HMP) algorithm. This algorithm performs message updates along the edges of the factor graph, and selectively applies either the variational message passing (VMP) or sum-product (SP) rules, depending on the factor-node structure and message tractability. Simulation results validate the effectiveness of the proposed column-wise hierarchical prior model through ablation studies and demonstrate that the MMV-HMP algorithm, while maintaining moderate computational complexity, consistently outperforms existing baselines which fail to capture the structured sparsity of wideband XL-MIMO channels.

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.

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 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.

In this paper, we investigate the question of which technology, fluid antenna systems (FAS) or active reconfigurable intelligent surfaces (ARIS), plays a more crucial role in FAS-ARIS wireless communication systems. To address this, we develop a comprehensive system model and explore the problem from an optimization perspective. We introduce an alternating optimization (AO) algorithm incorporating majorization-minimization (MM), successive convex approximation (SCA), and sequential rank-one constraint relaxation (SRCR) to tackle the non-convex challenges inherent in single-user scenario. Specifically, for the transmit beamforming of the BS optimization, we propose a closed-form rank-one solution with low-complexity. For the optimization the positions of fluid antennas (FAs) of the BS, the Taylor expansions and MM algorithm are utilized to construct the effective lower bounds and upper bounds of the objective function and constraints, transforming the non-convex optimization problem into a convex one. Furthermore, we use the SCA and SRCR to optimize the reflection coefficient matrix of the ARIS and effectively solve the rank-one constraint. To be more general, the proposed AO algorithm is then extended to multi-user scenario. Simulation results reveal that the relative importance of FAS and ARIS varies depending on the scenario: FAS proves more critical in simpler models with fewer reflecting elements or limited transmission paths, while ARIS becomes more significant in complex scenarios with a higher number of reflecting elements or transmission paths. Ultimately, the integration of both FAS and ARIS creates a win-win scenario, resulting in a more robust and efficient communication system. This study underscores the importance of combining FAS with ARIS, as their complementary use provides the most substantial benefits across different communication environments.

Metamaterial antennas are appealing for next-generation wireless networks due to their simplified hardware and much-reduced size, power, and cost. This paper investigates the holographic multiple-input multiple-output (HMIMO)-aided multi-cell systems with practical per-radio frequency (RF) chain power constraints. With multiple antennas at both base stations (BSs) and users, we design the baseband digital precoder and the tuning response of HMIMO metamaterial elements to maximize the weighted sum user rate. Specifically, under the framework of block coordinate descent (BCD) and weighted minimum mean square error (WMMSE) techniques, we derive the low-complexity closed-form solution for baseband precoder without requiring bisection search and matrix inversion. Then, for the design of HMIMO metamaterial elements under binary tuning constraints, we first propose a low-complexity suboptimal algorithm with closed-form solutions by exploiting the hidden convexity (HC) in the quadratic problem and then further propose an accelerated sphere decoding (SD)-based algorithm which yields global optimal solution in the iteration. For HMIMO metamaterial element design under the Lorentzian-constrained phase model, we propose a maximization-minorization (MM) algorithm with closed-form solutions at each iteration step. Furthermore, in a simplified multiple-input single-output (MISO) scenario, we derive the scaling law of downlink single-to-noise (SNR) for HMIMO with binary and Lorentzian tuning constraints and theoretically compare it with conventional fully digital/hybrid arrays. Simulation results demonstrate the effectiveness of our algorithms compared to benchmarks and the benefits of HMIMO compared to conventional arrays.

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.

The phase shift information (PSI) overhead poses a critical challenge to enabling real-time intelligent reflecting surface (IRS)-assisted wireless systems, particularly under dynamic and resource-constrained conditions. In this paper, we propose a lightweight PSI compression framework, termed meta-learning-driven compression and reconstruction network (MCRNet). By leveraging a few-shot adaptation strategy via model-agnostic meta-learning (MAML), MCRNet enables rapid generalization across diverse IRS configurations with minimal retraining overhead. Furthermore, a novel depthwise convolutional gating (DWCG) module is incorporated into the decoder to achieve adaptive local feature modulation with low computational cost, significantly improving decoding efficiency. Extensive simulations demonstrate that MCRNet achieves competitive normalized mean square error performance compared to state-of-the-art baselines across various compression ratios, while substantially reducing model size and inference latency. These results validate the effectiveness of the proposed asymmetric architecture and highlight the practical scalability and real-time applicability of MCRNet for dynamic IRS-assisted wireless deployments.

The site-specific non-line-of-sight (NLoS) conditions and unpredictable transmission signals in urban areas complicate localization efforts. Radio-frequency fingerprinting (RFF) addresses this challenge by building a database of signal characteristics at various locations. However, transition from indoor to outdoor environments is difficult due to the vast physical area and inaccessible sites. In this paper, we propose an RFF-based passive localization approach enhanced by ray-tracing simulation. This simulation utilizes real geographic data, including all buildings and terrains, without making assumptions about NLoS error statistics. To improve localization accuracy, we investigate the optimal placement of a moving sensor by minimizing the average mean squared error (MSE) within a confidence interval. Simulation results indicate that RFF-assisted passive localization is effective in real-world scenarios, and the optimal placement of the moving sensor significantly enhances localization accuracy.

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

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach. Unlike traditional optimization approaches that suffer from exponential complexity growth, our DRL-based method achieves real-time decision-making with superior scalability for complex multi-target scenarios while maintaining computational efficiency.

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.

This paper characterizes the physical layer security (PLS) in a network utilizing massive multiple-input multiple-output (MIMO) for over-the-air computation (AirComp). When the direct links between the access point (AP) and the sensors are blocked, an intelligent reflecting surface (IRS) is employed to establish communication. Furthermore, the AP sends artificial noise (AN) to the eavesdropper to prevent wiretapping. We study the problem of minimizing the mean-square-error (MSE) between the original and intercepted signals subject to the transmit power constraints at the AP and the sensors, as well as how the MSE threshold hinders the eavesdropper under both perfect and imperfect channel state information (CSI). In the case of perfect CSI, obtaining a globally optimal solution for the investigated non-convex problem is challenging due to the optimization variables’ couple nature. Hence, we convert the problem into two sub-problems to obtain locally optimal solutions. One sub-problem can be solved by an exact penalty-based algorithm, while the other has a closed-form solution using the popular majorization-minimization (MM) algorithm. For the imperfect CSI, the robust beamforming optimization problem formulated is still non-convex. To address this, we harness the block coordinate descent (BCD) algorithm for alternately optimizing the variables to solve it. The results of our simulations demonstrate that the superior MSE performance exhibited by the proposed scheme.

The allocation of secure resources in non-orthogonal multiple access (NOMA) systems has gained significant recognition as a vital research focus in the realm of the Internet of Things (IoT). Previous studies have overlooked the security challenges associated with integrating NOMA with finite blocklength (FBL) transmission. Therefore, this paper examines a secure downlink NOMA system utilizing FBL transmission, which includes a base station (BS), a near user, a far user, and an external eavesdropper. We develop an optimization problem with the objective of maximizing the near user’s effective secrecy throughput, considering the secrecy rates, decoding error probabilities (DEPs), and effective secrecy throughput for both users. Notably, by meticulously defining the DEPs of the users as optimization variables, the monotonicity and concavity of these DEPs in relation to the blocklength, transmission power, and transmission rate can be established effectively. The problem is divided into two sub-problems focusing on the essential conditions for the secrecy rate of the near user, especially in scenarios where successive interference cancellation (SIC) is unsuccessful. These sub-problems are addressed using the block coordinate descent (BCD) algorithm and an exact penalty method. For comparison, the BCD algorithm is also applied to solve the optimization problem using the orthogonal multiple access (OMA) scheme. Numerical simulations confirm the effectiveness of our proposed approaches in improving secure resource allocation when NOMA is combined with FBL transmission.

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.

In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employing short-packet communications, where the two users are denoted by the central user (CU) and the cell-edge user (CEU), respectively, and an RIS is deployed to enhance signal quality. Specifically, compared to CEU, CU lies nearer from BS and enjoys the higher channel gains. Closed-form expressions for the CU’s average block error rate (BLER) are derived. Furthermore, we evaluate the CEU’s BLER performance utilizing selective combining (SC) and derive a tight lower bound under maximum ratio combining (MRC). Simulation results are provided to our analyses and demonstrate that the RIS-assisted system significantly outperforms its counterpart without RIS in terms of BLER. Notably, MRC achieves a squared multiple of the diversity gain of the SC, leading to more reliable performance, especially for the CEU. Furthermore, by dividing the RIS into two zones, each dedicated to a specific user, the average BLER can be further reduced, particularly for the CEU.

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

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

Incorporating short-packet communications with non-orthogonal multiple access (NOMA) networks is able to achieve both low communication delay and high spectral efficiency. In this article, a wireless NOMA downlink short-packet communication system is studied. The system includes a base station, an entrusted central user, and an untrusted cell-edge user. The untrusted cell-edge user may eavesdrop the signals from the base station to the central user. We theoretically derive the average secure block error rate (BLER) of the central user in flat Rayleigh fading channels by utilizing the linear approximations on BLER and secure BLER. We also present the asymptotic average secure BLER at high signal-to-noise ratio (SNR). From numerical results, it is shown that our derived analytical average secure BLER matches the simulated one. Numerical results also illustrate that at high SNR, error floors occur. Furthermore, at high SNR, it is found that when the average secure BLER is not one, the power allocation factors for the central and cell-edge users have almost no impact on the average secure BLER.