Search Results (315)

This work analyzes the ergodic capacity behavior of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems with a finite and arbitrary number of antennas and RIS elements under Nakagami-m fading conditions. By combining Hadamard’s determinant inequality with the Cauchy–Schwarz inequality, this work derives a dimensionally consistent closed-form upper bound on the ergodic capacity in terms of the Meijer G-function. Subsequently, it is demonstrated that at a high signal-to-noise ratio (SNR), a simplified expression for the capacity upper bound can be derived, enabling an analytical assessment of how the fading parameter influences the ergodic capacity. The study also explores the asymptotic behavior in the large-system regime, where the number of antennas or RIS elements tends to infinity. Monte Carlo (MC) simulations confirm the accuracy of the proposed bound and scaling laws. Full article

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks. Full article

This paper investigates an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with transmitter hardware impairments (HWIs) at the base station (BS). The active RIS provides both phase adjustment and amplitude amplification, which helps mitigate the multiplicative fading effect of passive RIS-assisted cascaded links and establish virtual line-of-sight (LoS) links for the target and multiuser communication users. Considering the coupling among the BS transmitter distortion noise, active RIS amplification noise, and the active RIS power constraint, we formulate a radar output signal-to-noise ratio (SNR) maximization problem. The radar output SNR is maximized by jointly designing the radar receive filter, the BS transmit beamforming matrix, and the active RIS reflection coefficients, while satisfying the quality-of-service (QoS) requirements of communication users, the BS transmit power constraint, and the active RIS power budget constraint. To solve the resulting non-convex problem with fractional objectives and high-order coupling terms, an alternating optimization (AO)-based iterative framework is developed. Specifically, the radar receive filter is updated using the generalized Rayleigh quotient, the BS transmit beamforming subproblem is handled by semidefinite relaxation and the Charnes–Cooper transformation, and the active RIS reflection coefficient subproblem is solved using the Dinkelbach transformation and majorization–minimization. Simulation results demonstrate stable convergence and show that the proposed design improves radar output SNR under BS transmitter HWIs. Full article

In wireless networks with severe blockage, path loss critically limits communication coverage. Reconfigurable Intelligent Surfaces (RIS) offer a promising remedy. However, the fine-grained control of massive reflecting elements incurs prohibitive computational overhead, which hinders real-time deployment. To address these challenges, this paper proposes a low-complexity scheme integrating RIS block selection with adaptive beamforming. The large-scale RIS is partitioned into multiple sub-arrays to enable block-wise phase control. By activating only those blocks with dominant channel gains, the system maximizes reflection gain while minimizing control overhead. To avoid the exponential complexity of exhaustive search, we develop a deep neural network (DNN)-based prediction architecture. By learning the mapping from channel states to optimal configurations, the DNN enables instantaneous selection of near-optimal RIS block combinations. Simulation results show that the proposed data-driven scheme achieves near-optimal bit error rate (BER) performance compared to exhaustive search. Notably, it avoids the exponential complexity growth typically associated with an increasing number of reflecting elements. The proposed mechanism extends reliable coverage range and improves link stability, offering an efficient solution for future wireless networks. Full article

Reconfigurable intelligent surfaces (RIS) equipped with extremely large aperture arrays (ELAA) are emerging as a key technology for enhancing beamforming gain, spatial multiplexing, and angular resolution in sixth-generation (6G) wireless networks. When combined with non-orthogonal multiple access (NOMA), RIS can further improve spectral efficiency, system throughput, and energy efficiency. However, most existing studies on RIS-aided NOMA assume far-field propagation, where the incident wavefronts are approximately planar. In contrast, RIS-ELAA systems operating at millimeter wave (mmWave) experience spherical wavefronts in the radiative near-field regions. Also, it creates spatial non-stationarity and distance-dependent phase curvature. These effects invalidate the conventional monotonic path-loss assumption and make fairness-oriented NOMA design more challenging. This paper proposes a joint element and power optimization (JEPO) algorithm for near-field RIS-ELAA-assisted indoor NOMA systems, in which the RIS is dynamically partitioned into user-specific subarrays performing near-field phase synthesis toward the near user (NU) and far user (FU). The reversed far-to-near successive interference cancellation (SIC) ordering, governed by an effective FU channel gain greater than an effective NU channel gain, is formally established, and a closed-form optimal power allocation is derived by reducing the max-min fairness condition to a scalar quadratic in the target signal-to-interference-plus-noise ratio (SINR), eliminating iterative power search. Simulation results confirm that JEPO consistently outperforms four baseline schemes across transmit power, NU distance, angular separation, and RIS aperture size, with the largest gain observed at ${\theta }_{\mathrm{NU}}\approx {40}^{\circ }$ where fixed-partition baselines collapse to near-zero fairness while JEPO maintains robust performance. Full article

Integrated sensing and communication (ISAC) technology enables the joint integration of communication and sensing functions through the efficient utilization of spectrum/energy resources. By further incorporating with the reconfigurable intelligent surfaces (RISs), the wireless propagation environment of ISAC systems can be dynamically controlled, thereby enhancing the overall sensing and communication (SAC) performance. In this context, accurate channel estimation is a fundamental prerequisite for the efficient operation of the RIS-assisted ISAC systems. However, the strong coupling between SAC signals from direct and reflected channel links, as well as severe noise interference, limits the SAC channel estimation accuracy. This paper proposes a novel two-stage channel estimation scheme for RIS-assisted ISAC systems, where the direct and reflected SAC channels are respectively estimated in the first and second stages. To mitigate the negative effects of noise interference in each estimation stage, an extreme learning machine-based feature purification module is precisely designed, improving the quality of received SAC signals and generating purified channel features. Then, the dedicated deep neural network adopts the purified channel features to estimate SAC channels. Simulation results demonstrate that, under different signal-to-noise ratio conditions and channel dimensions, the proposed scheme achieves superior estimation accuracy and strong robustness compared to the benchmark methods. Full article

Programmable electromagnetic environments, including reconfigurable intelligent surface (RIS)-assisted systems, are often described in terms of physical or controllable degrees of freedom. Such counts, however, do not determine which channel or operator directions can actually be distinguished by a finite measurement architecture. This paper develops an operator-space formulation of observable degrees of freedom for programmable propagation systems. We distinguish three nested layers: the physical operator space generated by the family of physically admissible propagation operators, the effective operator space selected by architectural constraints, and the observable subspace induced by a finite probing architecture. Once the effective space is fixed, observability is characterized by the spectrum of the associated measurement Gram operator. To remove arbitrary amplitude scaling, we introduce a common probe-energy normalization and define the resolution-dependent observable dimension $N_{\mathrm{obs}}\left(\eta \right)$ from the normalized Gram spectrum. The same spectrum also yields an observability condition number, which quantifies the stability of the visible subspace. We then extend the construction to symmetry-resolved operator spaces, showing how invariant probing can create sectorial blind subspaces and how controlled symmetry breaking produces second-order restricted visibility inside the original blind subspace. The mathematical ingredients are standard finite-dimensional tools from operator theory, frame theory, representation theory, and matrix concentration; the contribution is their integration into a measurement-oriented degrees-of-freedom framework for programmable electromagnetic environments. Numerical experiments with normalized probing families, sectorial decompositions, controlled symmetry breaking, and a canonical narrowband RIS-inspired model illustrate that architectures with the same effective dimension and probing budget can exhibit substantially different observable dimensions and conditioning. The results support the view that practical electromagnetic design should optimize not only the number of accessible modes or control states, but also the Gram geometry through which those directions are measured. Full article

A reconfigurable intelligent surface (RIS) is a promising technology for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless communications, but its passive reflection and two-hop double-fading structure make cascaded channel estimation challenging. Conventional convolutional neural network (CNN) estimators require many trainable parameters, while a single shallow parameterized quantum circuit (PQC) may have limited feature representation. Deep quantum circuits can also suffer from noise and barren-plateau effects on noisy intermediate-scale quantum (NISQ) devices. To address these issues, this paper proposes a multi-filter quantum convolutional neural network (MF-QCNN) for cascaded channel estimation in RIS-assisted multi-user uplink systems. The proposed model uses multiple independent shallow PQC filters in parallel, concatenates their measured features, and estimates the cascaded channel through a compact classical dense head, with the total trainable-parameter count scaling as $182F+696$ for F parallel filters. Simulation results, compared with a single-filter quantum convolutional neural network (QCNN), CNN, and multilayer perceptron (MLP) baselines, show that at a signal-to-noise ratio (SNR) of 20 dB, the 3-filter MF-QCNN reduces the normalized mean squared error (NMSE) by approximately 22.9, 8.1, and 4.6 dB relative to the single-filter QCNN, CNN, and MLP baselines, respectively, while using only about 19.3% of the CNN trainable parameters. Under zero-forcing (ZF) precoding, it achieves the highest achievable sum rate among the learning-based estimators; at SNR = 30 dB, it improves the achievable sum rate by approximately 17.4% and 12.8% over the CNN and MLP baselines, respectively. These simulation results suggest that the parallel shallow-PQC design can serve as a compact quantum-aided estimator for RIS channel estimation and may provide a useful basis for future studies on AI-native transceiver design in B5G/6G networks. Full article

This article introduces the reconfigurable intelligent surface (RIS) to physical layer authentication (PLA) designs to explore the utility of RIS in both the radio frequency fingerprint (RFF)/channel fingerprint (CF)-based PLA technique and the tag embedding (TE)-based PLA technique. Two new PLA schemes are proposed, i.e., the controllable reflection-based PLA (CR-PLA) scheme and the watermark hopping-based PLA (WH-PLA) scheme, where the role of RIS is discussed and analyzed carefully. First of all, considering the performance of RFF/CF-based PLA technique is degraded by the inaccurate feature estimation, the CR-PLA scheme is proposed to improve the feature estimation accuracy and to amplify the estimation differences among multiple devices through reconfiguring the wireless propagation channel. Then, to improve the performance of the TE-based PLA technique and introduce it to the RIS-aided systems, the WH-PLA scheme is developed. This scheme adds the security information on the pilot signal or message signal alternatively for authentication according to a designed pseudorandom embedding sequence with high uncertainty and randomness. Our simulation results verify the better performance of the proposed schemes compared with the existing schemes. The challenges and open issues of PLA designs in the RIS-aided wireless communication systems are also presented. Full article

Wireless links in underground coal mines suffer from severe attenuation, blockage, and limited spatial coverage. To improve link quality under these conditions, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted system with multiple movable antennas (MAs) installed at the base station (BS) panel. Unlike prior models that assume a continuous movement box, we explicitly account for practical panel constraints: mechanical supports and RF feed lines partition the BS panel into non-overlapping irregular feasible subregions. This turns the BS-side antenna-positioning task into a mixed-integer nonlinear program (MINLP). We formulate a joint optimization problem that couples BS beamforming, STAR-RIS transmission/reflection coefficients, BS-side MA positions, and MA-to-subregion assignment with collision-avoidance constraints. To solve it, we adopt a block coordinate descent (BCD) framework: successive convex approximation (SCA) for beamforming, semidefinite relaxation (SDR)-based updates for STAR-RIS coefficients, and a penalty-based continuous relaxation for MINLP handling. The MA solver further integrates Hungarian initialization, cross-region jump updates, and reassignment corrections to escape poor local subregions. Simulation results in coal mine channel settings show that the proposed method yields a 66.7% sum-rate gain over fixed-antenna baselines and reduces required transmit power by 16.8 dB at the target-rate operating point. Compared with a regular-region BS-MA baseline, the irregular-partition design achieves an additional 5.6 dB power saving, demonstrating the practical value of hardware-aware geometry modeling. Full article

This paper investigates an RIS-assisted mobile edge computing (MEC) system without reliable direct links between users and base stations (BSs). Users offload tasks to BSs through reconfigurable intelligent surface (RIS)-reflected links, where offloading decisions, service prices, and RIS-assisted transmission quality are tightly coupled. We formulate a joint design problem that considers task latency, transmission energy consumption, service pricing, BS computing constraints, and RIS phase-shift constraints. The RIS phase shifts are first optimized to improve the effective cascaded channel gain. Then, a distributed price-negotiation-based offloading mechanism is developed to coordinate user association and service pricing under channel-dependent utilities. Analysis and simulations show that the proposed algorithm converges within a finite number of iterations and achieves a balanced tradeoff between user utility and BS revenue. Full article

This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission–reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor–critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security–latency trade-off in complex urban vehicular scenarios. Full article

With the wide application of artificial intelligence (AI) in the Internet of Vehicles (IoV), IoV is under pressure for data transmission and real-time sensing. Integrated sensing and communication (ISAC) is one of the key technologies to alleviate that pressure. Obstacles can cause communication disruptions and increased delays, hindering autonomous driving information acquisition and causing traffic hazards. The application of Reconfigurable Intelligent Surfaces (RISs) aims to solve this problem. This study focuses on RIS-assisted multi-base station (MBS) scenarios in the presence of obstacles. This study aims to maximize the communication rate, minimize the sensing error, and reduce the switching frequency by optimizing the RIS phase shift and beamforming. The problem is modeled as mixed integer nonlinear programming (MINLP) and further described as a Markov Decision Process (MDP). We use Long Short-Term Memory (LSTM) to predict the environmental state and propose two optimization algorithms, Multi-Factor Decision Deep Deterministic Policy Gradient (MFD-DDPG) and Mixed Discrete and Continuous Action DDPG (MDCA-DDPG). In the first algorithm, we consider multiple factors to make a switching decision and use DDPG to yield the optimal action. The second algorithm improves DDPG by outputting a discrete switching decision and a continuous optimized action simultaneously. Simulations show that the proposed algorithms significantly improve the system performance, and the communication rate is increased by more than 40% in specific multi-vehicle scenarios compared to the benchmark. Full article

This paper investigates attack- and channel-aware decision fusion for Reconfigurable Intelligent Surface (RIS)-enhanced Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) to mitigate the challenge from Byzantine attacks. Specifically, we first propose the optimal hard decision fusion rule for the Fusion Center (FC) based on maximum-likelihood criterion, which simultaneously accounts for channel impairments and statistical characteristics of Byzantine attacks. Following from this result, we then derive three suboptimal and low-complexity decision fusion rules when the Channel State Information (CSI) cannot be perfectly achieved at the FC. The correspondingly results indicate that negative weighting coefficients can be adaptively assigned to malicious reports based on attack intensity, which can successfully transform adversarial interference into effective detection gains for the FC in some scenarios. This finding profoundly reveals the intrinsic mechanism of how Byzantine attacks impact the decision fusion, and thus provide a rigorous theoretical perspective for developing robust decision fusion rule capable of adaptively suppressing and conversely exploiting malicious reports. Furthermore, to make practical implementation of our decision fusion rules, we develop simple and unbiased attack parameter estimation algorithms based on the first-order statistics of received reports at the FC, which also exhibits good convergence. Our results indicate that we can insert a virtual source under control, and send false data to the Byzantine attackers. This deception strategy can help the FC successfully learn the attack parameter aided by its collected data. Finally, extensive simulations are conducted and the correspondingly results demonstrate that our proposed fusion rules can effectively mitigate Byzantine attacks across a wide range of attack scenarios, and they can outperform traditional malicious report filtering defense algorithm by successfully reversing and exploiting malicious reports. Full article

One of the most intensively developed areas of wireless telecommunications in recent years is the practical application of reconfigurable intelligent surfaces (RISs). This paper presents the results of experimental research conducted at 5.5 GHz using a 16 × 16 element RIS to verify the accuracy of the Tang, Zheng, and Jeong theoretical models, which describe signal behavior upon reflection from an RIS. In contrast to purely simulation-based papers, this study utilizes different antenna types in a laboratory environment representative of a typical office space. The performance of the models is evaluated across distances of 1 m, 1.5 m, and 2 m through a comprehensive quantitative analysis. The study reports core error metrics, including mean and median root mean square error (RMSE), mean absolute error (MAE), and sum of squared differences (SSD), as well as the Interquartile Range (IQR) to assess modeling stability. Furthermore, a Wilcoxon signed-rank test is employed to statistically compare the modeling accuracy. Full article