evaluate ris frequency selectivity

This study reveals that reconfigurable intelligent surfaces (RIS), while optimizing a primary communication link, can induce significant unintended interference on nearby secondary links—even when the two are spatially separated and operate on distinct carrier frequencies. Through real-world experiments conducted in the FR1 band using the CorteXlab platform and Greenerwave RIS hardware, the work systematically evaluates the impact of RIS configurations on the received power and channel phase of secondary links under representative coexistence scenarios, including both co-channel and inter-channel conditions. The experiments provide the first empirical evidence that conventional assumptions relying on frequency-domain isolation to mitigate interference no longer hold in RIS-enabled environments, thereby underscoring the critical need for cross-link compatibility considerations in RIS deployment strategies.

The absence of a standardized system-level simulation framework hinders rigorous performance evaluation of Reconfigurable Intelligent Surfaces (RIS) in 6G multi-RIS, multi-base-station networks. Method: This work establishes the first 3GPP-compliant system-level simulator for RIS-aided networks. It integrates geometrically random RIS deployment, configurable panel density and size, and—novelly within a standardized framework—jointly models key non-ideal hardware effects: near-field propagation, inter-panel interference, phase quantization error, and unit failure. These factors are rigorously incorporated into path loss and large-scale fading modeling for both RIS-reflected and direct links. Results: Simulation results demonstrate that strategic RIS deployment significantly enhances Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), spectral efficiency, and cell coverage. The platform provides a reproducible, quantitative performance benchmark and design guidelines to support standardization of RIS technology in 6G systems.

This work addresses the challenges of quantifying reconfigurable intelligent surface (RIS) reflection characteristics in realistic propagation environments and the lack of empirical foundations for codebook design. We conduct the first systematic, multi-dimensional field measurement study, jointly characterizing coupling effects among key parameters—including azimuth and elevation angles, polarization, number of RIS elements, and transceiver distance. Leveraging measurement-driven modeling and multi-parameter joint sweep measurements, we construct a high-fidelity RIS reflection response database. Subsequently, we perform reflection pattern characterization and codebook feasibility analysis. Results demonstrate the practical viability of designing low-complexity, high-performance RIS codebooks for typical deployment scenarios. This study establishes the first methodology and dataset—grounded in full-dimensional, real-world measurements—for RIS channel modeling and beamforming tailored to practical implementation.

This study addresses the modeling mismatch of reconfigurable intelligent surfaces (RIS) between far-field and near-field regimes by establishing a unified channel model that systematically incorporates line-of-sight/non-line-of-sight propagation, scattering environment richness, channel correlation, and array manifold effects. It is the first to reveal the fundamental impact of near-field spherical wavefronts on reflected beam design. A hybrid beamforming framework—balancing accuracy and computational complexity—is proposed, comprising both optimization-driven and closed-form analytical design methods. Leveraging electromagnetic modeling, convex optimization, and parametric sensitivity analysis, the work quantifies, via simulation, the boundary effects of antenna spacing, operating distance, and carrier frequency on RIS gain, focusing accuracy, and user coverage. The results provide both theoretical foundations and practical design guidelines for near-field RIS systems.

Existing RIS research is largely confined to theoretical modeling and physical-layer analysis, lacking experimental validation that integrates realistic channel characteristics and full protocol-stack implementation—thus hindering cross-band (Sub-6 GHz/mmWave) and multi-scenario (eMBB/URLLC) performance evaluation. Method: This work presents the first full protocol-stack hardware-in-the-loop experiment for RIS-empowered systems: (i) a geometry-based stochastic channel model built upon QuaDRiGa; (ii) a single-leader–multiple-followers Stackelberg game framework jointly optimizing RIS phase shifts and power allocation. Contribution/Results: The framework is validated on Colosseum—the world’s largest wireless emulation platform—enabling the first end-to-end RIS stack evaluation. Results demonstrate substantial energy efficiency gains at mmWave frequencies; at Sub-6 GHz, a compact RIS of only ~100 elements significantly reduces URLLC latency, highlighting its superiority in resource-constrained deployments.

Enhancing cellular coverage in urban environments remains challenging due to complex propagation conditions and infrastructure constraints. Method: This paper proposes the first fully automated, data-driven framework for joint optimization of reconfigurable intelligent surface (RIS) placement, orientation, configuration, and base station beamforming—compatible across 4G/5G/6G bands. It innovatively fuses real-world channel measurements with a physics-consistent ray-tracing model (built on Sionna), augmented by reflection/scattering-aware heuristic filtering, outage-user clustering, and multi-variable electromagnetic optimization to drastically reduce deployment complexity. Results: Experiments reveal that substantial coverage gains in dense urban settings require closely spaced, large-aperture RISs—yet diminishing returns impose cost-effectiveness bottlenecks. The framework is open-sourced, providing a reproducible methodology and benchmarking toolkit for scalable RIS deployment.

Reconfigurable Intelligent Surfaces (RIS) face critical security and privacy challenges in real-world deployments—such as smart homes, vehicular networks, and industrial IoT—yet lack a systematic, system-level threat model. Method: This work establishes the first practical RIS-specific threat model, encompassing diverse attacker scenarios under both legitimate and malicious RIS configurations. It systematically identifies six novel security vulnerabilities, characterizes RIS-enabled physical-layer attacks—including eavesdropping, jamming, and spoofing—and proposes a user-initiated auxiliary-RIS defense paradigm. Contribution/Results: The study synthesizes twelve cross-layer defense strategies and releases the first open-source RIS security repository, featuring a comprehensive toolchain, empirically collected datasets, and scenario-based demonstrations. Collectively, this work provides foundational theoretical insights and empirical groundwork for RIS security standardization and practical deployment.

Conventional RISs are constrained by diagonal scattering matrices, limiting reconfigurability; while emerging branch-decoupled RISs (BD-RISs) enable inter-element connections, existing studies rely on oversimplified channel models that neglect critical electromagnetic effects—such as mutual coupling and impedance mismatch. Method: Leveraging physically consistent multi-port network theory, we establish an exact electromagnetic model for BD-RISs and, for the first time, prove that a connected BD-RIS achieves equivalent channel manipulation capability to a fully connected RIS under this rigorous model. We further propose a unified joint optimization framework integrating closed-form solutions, semidefinite relaxation (SDR), and the alternating direction method of multipliers (ADMM), applicable to SISO, single-stream, and multi-user MIMO scenarios. Results: Experiments quantify the substantial performance impact of mutual coupling modeling and the unidirectional approximation, providing both theoretical foundations and computationally efficient tools for practical RIS architecture design and deployment.

This work addresses the feasibility and cost bottlenecks of deploying Reconfigurable Intelligent Surfaces (RIS) in urban cellular networks. Methodologically, it introduces a fully automated joint optimization framework grounded in calibrated ray-tracing digital twins, integrating Sionna-based ray tracing, empirical channel measurement calibration, electromagnetic RIS modeling, and multi-band (4G/5G/6G) beamforming. Candidate RIS locations are identified via scattering-ray analysis, while user clustering reduces deployment scale. Its key contributions include the first cross-generation frequency-band co-optimization and quantitative assessment of large-scale RIS deployment necessity. Results demonstrate that substantial coverage gains require high-density, large-aperture RIS configurations—highlighting fundamental feasibility and economic viability challenges in practical deployment. The framework establishes a scalable, empirically verifiable digital twin paradigm for RIS network planning.

To address the high secrecy outage probability (SOP) in physical-layer security, this paper proposes a Fluid Reconfigurable Intelligent Surface (FRIS) architecture. Unlike conventional planar or compact RISs, FRIS dynamically reconfigures both the spatial distribution of its unit elements and their phase responses to exploit low spatial correlation and thereby enhance secrecy capacity. Methodologically, we jointly model the end-to-end channel via maximum likelihood estimation (MLE) and optimize the FRIS layout adaptively using Q-learning, while co-designing beamforming and phase control for closed-loop performance enhancement. Experimental results demonstrate that FRIS significantly reduces SOP even without phase optimization; compared to compact RISs, its spatial flexibility enables superior spatial decorrelation and more robust secrecy gains. This work establishes a new paradigm for secure wireless communications in dynamic environments.