Search Results (352)

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

This study introduces a switchable and tunable multimodal, multi-peak, perfect terahertz absorber, utilizing a composite structure of graphene and double concentric metal rings. From bottom to top, the absorber consists of a gold substrate, a SiO₂ dielectric layer, a patterned graphene layer, another SiO₂ dielectric layer, and double concentric metal rings on the top. The structure achieves three high-absorption resonance peaks in the far-infrared band: a relatively broad peak with 99.05% absorptance at 38.128 THz, and two extremely narrow peaks with 99.56% and 97.23% absorptance at 47.909 THz and 49.873 THz, respectively. Analysis of the absorption spectra and electric field distributions reveals that the generation mechanism of Peak I is Fabry–Pérot cavity resonance, while Peaks II and III result from the coupling between the high-order localized surface plasmons in the outer ring and the graphene surface plasmon polaritons. Benefiting from graphene’s excellent electrical tunability, the absorption peaks’ positions and intensities can be dynamically tuned by varying the Fermi level. The core innovation of this work lies in the high-level integration of multiple functionalities. By leveraging the sensitive response of Peak III to variations in the Fermi level, a four-input AND logic gate is embedded within the metamaterial absorber in this frequency band. The Fermi levels of four independent graphene regions serve as the binary inputs, while the absorption state of Peak III is defined as the logical output. Additionally, the two narrow peaks display high sensitivity to the surrounding refractive index, with sensitivities of 30.1 THz/RIU and 62.5 THz/RIU, demonstrating significant potential for sensing. This multifunctional integrated device combines tunable absorption, a logic gate, and sensing capabilities, making it promising for terahertz communication systems, intelligent sensing networks, and reconfigurable platforms. Full article

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

This paper proposes a new fuzzified multi-objective wireless communication optimization model that maximizes the quantity and placement of Reconfigurable Intelligent Surfaces (RISs). In order to meet realistic deployment constraints like non-overlapping and acceptable location, the model aims to decrease the number of deployed RISs while raising the achievable rate. The Modified Pied Kingfisher Optimization Algorithm (MPKOA) is suggested as a solution to this intricate optimization issue. MPKOA features many significant improvements over the traditional Pied Kingfisher Optimization Algorithm (PKOA), such as energy-based motion control, adaptive subgrouping, flock cooperation, and memory-driven re-perching. These techniques speed up convergence, improve solution precision, reduce computation time, and balance exploration and exploitation. MPKOA performs better than standard PKOA, Enhanced version of PKOA (EPKO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and other existing algorithms, according to extensive comparisons. MPKOA can achieve up to 20% higher optimization values and 30% faster convergence, according to simulation data. In addition, the proposed MPKOA reduces computational complexity and runtime by about 50% when compared to standard PKOA-based approaches since it only requires single fitness evaluation per iteration. This enables the deployment of fewer RISs while still achieving higher communication rates. In multiuser wireless systems, MPKOA offers a robust and effective approach to RIS placement optimization, which helps to boost capacity and provide more energy-efficient 6G communication networks. Full article

Reconfigurable intelligent surface (RIS) emerges as a promising paradigm and offers a new perspective for physical layer security. In practice, imperfect eavesdropper channel state information (CSI) represents a critical challenge for RIS-aided physical layer security design. To tackle this issue, this paper investigates RIS-aided physical layer security enhancement under imperfect eavesdropper CSI and formulates a robust weighted sum secrecy rate maximization problem. To efficiently solve this problem, a model-driven deep learning approach is proposed. We begin by introducing the gradient descent–ascent algorithm to solve the optimization problem. Then we unfold this algorithm into a gated recurrent unit (GRU)-aided deep unfold network with trainable parameters. The proposed GRU-aided deep unfold network leverages GRU to adaptively generate gradient ascent–descent step sizes. Different from the existing deep unfold network that commonly has a fixed number of iteration, the proposed deep unfold network integrates the sequential learning capability of GRU and enables adaptive iteration adjustment. The simulation results demonstrate that compared to existing non-robust optimization algorithm and traditional deep unfold network with fixed number of iteration, the proposed method exhibits robustness against imperfect CSI and achieves higher weighted sum secrecy rate. Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods. Full article

The theory of physical degrees of freedom (DoF) developed by Franceschetti–Migliore–Minero (FMM) establishes a fundamental phase transition in the singular-value spectrum of electromagnetic radiation operators under maximal rotational symmetry. In this work, we revisit this result from a symmetry-explicit operator-theoretic perspective and extend it to scenarios with reduced and controllable symmetries, with particular emphasis on reconfigurable intelligent surfaces (RISs). We model the radiation process as a compact operator acting between admissible source and observation spaces and characterize its symmetry through group equivariance. This formulation enables a systematic decomposition of the operator into irreducible representation sectors associated with the effective symmetry group, defined as the intersection of symmetries supported jointly by the source architecture, RIS geometry and programmability, receiver configuration, and propagation environment. We show that the FMM phase transition persists within each symmetry sector and that the total DoF budget is redistributed across sectors according to symmetry constraints. A key outcome of this analysis is the distinction between physical and effective degrees of freedom. While breaking the maximal $\mathrm{SO}\left(2\right)$ symmetry does not increase the total number of electromagnetic DoF dictated by physics, symmetry reduction modifies their allocation across sectors, potentially lifting degeneracies and increasing the number of degrees of freedom that can be effectively addressed by a given excitation, RIS control, and measurement architecture, even when the total number of physical DoF remains fixed by fundamental limits. This clarifies the role of controlled symmetry breaking as a design mechanism rather than a means to surpass fundamental limits. The proposed framework bridges electromagnetic operator theory, representation theory, and RIS-enabled system design, providing both rigorous symmetry-resolved DoF accounting and actionable insights for excitation, surface programmability, and measurement strategies under practical architectural constraints. Full article

To mitigate the loss of satellite navigation signals in indoor environments, we propose an active reconfigurable intelligent surface (ARIS)-empowered satellite positioning approach. Deployed on building structures, ARIS reflects navigation signals to indoor receivers to bypass obstructions, providing high-precision positioning services to receivers in non-line-of-sight (NLoS) areas. The path between ARIS and the receiver is defined as the extended line-of-sight (ELoS) path, and an improved carrier phase observation equation is derived to accommodate this path. The receiver compensates for its clock bias through network time synchronization, corrects the actual satellite–ARIS–receiver signal path to the satellite–receiver distance through a distance correction algorithm, and determines the position using the least squares (LS) method. Simulation results show that the proposed method provides positioning services with errors not exceeding 4 m in indoor environments, with time synchronization accuracy within an error range of 10 ns. Full article

This paper develops an intelligent Enhanced Starfish Optimization (ESFO) algorithm for optimizing a secure wireless communication infrastructure. The Starfish Optimization (SFO) algorithm is inspired by starfish biology, using the integrated modeling of the arm-based exploration, preying, and regeneration behaviors of starfish. To further enhance the exploitation capability of the standard Starfish Optimization (SFO), the proposed Enhanced Starfish Optimization (ESFO) integrates a fitness-based interacting mechanism within the exploitation phase. This innovative modification improves local search accuracy, preserves population diversity, and mitigates premature convergence without introducing additional control parameters. Moreover, the proposed Enhanced Starfish Optimization (ESFO) is designed for secure wireless transmission, which is considered one of the main topics in next-generation wireless network infrastructure. The investigated network addresses the use of Simultaneously Transmitting and Reflecting RIS (STAR-RIS) in the security of the physical layer. This implemented STAR-RIS has a coupled phase shift to create reflected and transmission links, unlike traditional Reconfigurable Intelligent Surface (RIS). In this regard, we create a safe beamforming architecture that optimizes both Base Station (BS) precoding vectors and STAR-RIS transmission/reflection coefficients. In order to validate the efficiency of the proposed Enhanced Starfish Optimization (ESFO) algorithm, it is compared to several benchmark optimizers such as standard Starfish Optimization (SFO), Dhole Optimizer (DO), Neural Network Algorithm (NNA), Crocodile Ambush Optimization Algorithm (CAOA), and white shark Optimizer (WSO). These comparisons include several scenarios based on the transmitted power threshold which is varied in the range of 20 to 70 dBm with step of 5 dBm. The simulation results show that the proposed Enhanced Star Fish Optimization (ESFO) algorithm consistently outperforms existing benchmark approaches. This study supports future intelligent communication infrastructures in terms of secrecy and achievable rates over a range of transmit power levels. In particular, ESFO improves performance by up to 20–25% while converging 40–50% faster than traditional optimization algorithms, demonstrating its usefulness and resilience in STAR-RIS-assisted secure communication systems. The suggested ESFO-enabled architecture outperforms standard RIS-based systems in terms of secrecy capacity, according to numerical studies, and low-resolution STAR-RIS phase-shifters are sufficient to ensure robust secrecy performance. Full article

Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input multiple-output (MIMO) systems further exacerbates power consumption and hardware costs. To address these challenges, this paper investigates RIS-assisted millimeter-wave (mmWave) MIMO systems with low-resolution analog-to-digital converters (ADCs). Exploiting the inherent sparsity of mmWave channels and considering the distortion introduced by low-resolution quantization, we propose a compressive sensing (CS)-based channel estimation scheme. Furthermore, to mitigate the effects of angular leakage, we introduce an energy capture orthogonal matching pursuit (ECOMP) algorithm. Simulation results demonstrate that the proposed scheme not only improves channel estimation accuracy but also reduces pilot overhead and power consumption, while maintaining enhanced stability in high signal-to-noise ratio (SNR) regimes. Full article

This paper considers design optimization for an active reconfigurable intelligent surface (active RIS)-aided cognitive multigroup multicast communication system. To minimize the sum of the weighted power of the cognitive radio base station (CRBS) and active RIS, the joint design problem of CRBS matrix and active RIS reflection coefficients is discussed, satisfying the constraints of the received signal-to-interference-plus-noise ratio (SINR), the maximum gain constraints of the active RIS, and the interference constraints on the primary users (PUs). Due to the complex coupling and non-convex nature of decision variables in the objective function and constraints, the decision variables were decoupled using the alternate optimization (AO) method, and then methods such as the successive convex approximation (SCA), Schur complement, and penalty convex–concave procedure (PCCP) were utilized to transform the non-convex constraints into tractable convex forms. Finally, an efficient algorithm based on AO for the cognitive multigroup multicast system was proposed, which can reduce total system power consumption by at least 9% compared to a passive RIS (P-RIS). Numerical results identify the system parameter conditions under which the designed system and the proposed algorithm outperform the benchmarks and portray how the system performance is affected by changes to the system parameters. Full article

Sea-surface reflections and wind–wave motion render maritime channels strongly time-varying and statistically non-stationary, while nearshore deployments face sparse infrastructure and co-channel multiuser interference. This study integrates reconfigurable intelligent surfaces (RISs) with rate-splitting multiple access (RSMA) for joint online resource allocation. A physics-inspired time-varying channel model is established by embedding fractional Brownian motion-driven slow statistical drift and reflection-phase perturbations. With imperfect, delayed channel state information (CSI) and discrete RIS phase quantization, a proportional-fairness utility maximization problem is formulated to jointly optimize shore base-station precoding, RIS phase shifts, and RSMA common-rate allocation. To cope with strong non-convexity, high dimensionality, mixed continuous–discrete coupling, and partial observability, a fractal-aware recurrent robust Actor–Critic (FRRAC) algorithm is developed. FRRAC encodes short observation histories using a gated recurrent unit and incorporates a lightweight Hurst-proxy estimator to capture slow channel statistics for robust value evaluation and policy learning. Truncated quantile critics and mixed prioritized–uniform replay further improve value robustness, training stability, and sample efficiency. Simulation results show that FRRAC converges faster and more stably under both conventional and fractal non-stationary channel modeling, and outperforms representative baselines across the objective and multiple statistical metrics, validating its effectiveness for joint resource optimization in maritime RIS–RSMA systems. Full article

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has emerged as a promising technique to enhance spectral efficiency and coverage in fifth- and sixth-generation wireless networks. However, asymmetric indoor propagation conditions characterized by heterogeneous line-of-sight (LoS) and non-line-of-sight (NLoS) links often degrade user fairness. This paper investigates a downlink RIS-assisted NOMA system under the standardized 3GPP indoor office (InH) channel model to address fairness-oriented design under realistic link-budget constraints. We formulate an optimization problem for max–min fairness that jointly considers discrete RIS element partitioning and NOMA power allocation to achieve a symmetrical allocation of quality of service (QoS). To enable efficient computation, the non-convex problem is transformed into an epigraph form and solved using a low-complexity, bisection-based quasi-convex optimization framework combined with enumeration over RIS partitions. Numerical results demonstrate significant fairness gains; for instance, doubling the RIS array size yields a substantial improvement in the ergodic max–min rate, corresponding to approximately a 66% gain at moderate transmit power levels. Furthermore, by accounting for practical impairments such as imperfect successive interference cancellation (iSIC), imperfect channel state information (iCSI), and RIS implementation losses, the results reveal that fairness-optimal operation consistently prioritizes the far user to overcome severe indoor NLoS attenuation. The proposed framework is also compared with alternating optimization (AO)-based RIS-NOMA, conventional RIS beamforming without partition and RIS-assisted orthogonal multiple access (OMA) schemes. Simulation results confirm that the proposed framework achieves low computational complexity, making it suitable for practical indoor wireless environments. Full article

The increasing demand for global connectivity has accelerated the integration of non-terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite constellations, into next-generation position navigation and time (PNT) systems. While LEO-based PNT offers low-latency and high-accuracy potential, challenges such as high path loss and limited ground-level signal diversity remain. Reconfigurable intelligent surfaces (RISs) have emerged as a cost-effective solution to enhance localization performance by providing controllable reflections with minimal infrastructure. Building on prior work in single-RIS NTN scenarios, this paper investigates RIS-aided localization in a single-LEO PNT setting with multiple RISs. We introduce a detailed signal model and multi-stage processing framework that estimates both the satellite and RIS-assisted paths, enabling accurate receiver localization. Simulations assess the trade-offs in coverage and accuracy, providing insights into the feasibility and optimization of RIS-assisted NTN PNT solutions as a complementary alternative to global navigation satellite system (GNSS). Full article

This paper proposed a reconfigurable intelligent surface (RIS)-empowered reference-modulated differential chaos shift keying (RM-DCSK) wireless powered communication (WPC) system. As a noncoherent chaotic communication scheme, the proposed system exploits the reference reuse property of RM-DCSK, where the reference signal simultaneously carries data information, thereby improving spectral efficiency while maintaining noncoherent and channel-estimation-free reception with low receiver circuit complexity. Furthermore, RIS is utilized to reconfigure the propagation environment and mitigate the path loss effect of WPC links. At the user equipment (UE), a harvest–store–use (HSU) energy harvesting and finite-buffer model is developed, and a threshold-based on/off transmission policy is adopted to enable sustainable uplink transmission. To quantify the gain of energy buffering and management, a bufferless baseline system is further established. Closed-form bit error rate (BER) expressions are obtained under multi-path Rayleigh fading channels for both the proposed RIS-RM-DCSK-WPC system and bufferless baseline system. Finally, simulation results validate the analysis and demonstrate that the proposed system achieves superior BER performance compared with representative benchmarks, including existing RIS-aided DCSK-WPC, RM-DCSK-WPC, and bufferless RIS-RM-DCSK-WPC systems. Full article