Existing research on reconfigurable intelligent surface (RIS) beamforming in wireless communications predominantly assumes uniform phase quantization across reflecting units. However, in practical applications, engineering challenges and design requirements often lead to non-uniform phase and bit resolution of RIS units, which significantly limits the performance potential of conventional approaches. Current optimization frameworks struggle to effectively handle this non-uniform phase discretization due to the inherent non-convexity and high-dimensional nature of the resultant beamforming problem. To address this issue, this paper pioneers the study of discrete non-uniform phase configuration in RIS-assisted multi-user communication and formulates an optimization model to rigorously define the problem. For single-user scenarios, the paper proposes a partition-and-traversal (PAT) algorithm that efficiently achieves the global optimal solution through systematic search and traversal. For larger-scale multi-user scenarios, to further balance performance and computational complexity, a parameter tuning mechanism is introduced into the PAT algorithm, enhancing its flexibility and scalability. This mechanism optimizes the search strategy, significantly reduces computational overhead, and achieves linear complexity. Numerical simulations confirm the effectiveness and superiority of the proposed PAT algorithm. Additionally, we provide a detailed analysis of the impact of non-uniform phase quantization on the system performance.

This paper investigates the capabilities and effectiveness of backward localization centered on reconfigurable intelligent surfaces (RISs). In the backward sensing paradigm, the region of interest (RoI) is illuminated using a set of diverse radiation patterns. These patterns encode spatial information into a sequence of measurements, which are subsequently processed to reconstruct the RoI. We show that a single RIS can estimate the direction of arrival of incident waves by leveraging configurational diversity, and that the spatial diversity provided by multiple RISs further improves the accuracy of source localization and power estimation. The underlying structure of the sensing operator in the multi-snapshot measurement process is clarified. For single-RIS localization, the sensing operator is decomposed into a product of structured matrices, each corresponding to a specific physical process: wave propagation to and from the RIS, the relative phase offsets of elements with respect to the reference point, and the applied phase configuration of each element. A unified framework for identifying key performance indicators is established by analyzing the conditioning of the sensing operators. In the multi-RIS setting, we derive–via rank analysis–the governing law among the RoI size, the number of elements, and the number of measurements. Upper bounds on the relative error of the least squares reconstruction algorithm are derived. These bounds clarify how key performance indicators affect estimation error and provide valuable guidance for system-level optimization. Numerical experiments confirm that the trend of the relative error is consistent with the theoretical bounds. Finally, we develop a proof-of-concept prototype using universal software radio peripherals and employ a magnitude-only reconstruction algorithm tailored to the system. To the best of our knowledge, this represents the first experimental demonstration of its kind.

Reconfigurable intelligent surfaces (RISs) hold significant potential for enhancing coverage and data rates in 6G wireless communication systems. While most research has concentrated on reflective RIS applications, studies on transmissive RIS (TRIS) have been largely limited to simulations or laboratory prototypes. To evaluate the real-world performance of TRIS, we developed a 256 -unit cell, 1-bit TRIS prototype operating at the 5.8 GHz frequency band. The design employs an antisymmetric configuration of two PIN diodes, achieving nearly uniform transmission amplitude with inverse phase states over a wide 20 % bandwidth. The fabricated TRIS, composed of$16 \times 16$unit cells, demonstrates effective 1-bit phase tuning with minimal insertion loss and a 3 dB transmission bandwidth exceeding 1.2 GHz. By dynamically modulating the quantized code distributions, we achieved scanning beams of$\pm 60^{\circ}$. Subsequently, we integrated the TRIS into a Wi-Fi wireless communication system and assessed its performance in a real-world wall-penetration scenario. The TRIS improved the average Signal-to-Noise Ratio (SNR) by 8 dB, with a maximum gain of 15 dB, across a 29$\mathbf{m}^{2}$indoor area. Download speeds also increased, showing a maximum improvement of 10.57 Mbps and an average gain of 8.01 Mbps. These results highlight the effectiveness of TRIS in reducing path loss, enhancing SNR, and improving WiFi performance in challenging wall-penetration environments, ultimately leading to a better user experience.

Human activity recognition (HAR) using radio frequency (RF) signals has attracted increasing interest due to its non-intrusive and privacy-preserving nature. However, traditional systems often suffer from multipath fading, environmental noise, and limited spatial diversity, particularly in through-the-wall scenarios. In this paper, we propose TRIS-HAR, a novel HAR system that integrates a transmissive reconfigurable intelligent surface (TRIS) with an advanced dual-stream state space model, Human intelligence Mamba (HiMamba). The TRIS actively reshapes the propagation environment by constructing deterministic quasi-line-of-sight (QLoS) paths across obstacles, significantly improving channel state information (CSI) quality. Complementing this, HiMamba leverages a lightweight structured state space architecture to jointly model temporal and spectral dynamics, enabling robust activity recognition under non-line-of-sight conditions. Extensive experiments on both public and real-world datasets demonstrate that TRIS-HAR improves recognition accuracy from 85.00% to 98.06% and maintains strong generalizability across environments. The model is also deployed on a CPU-based edge device, achieving real-time inference at 108 FPS with minimal memory cost. This work establishes a co-designed hardware-algorithm framework for RF-based HAR, offering a scalable and deployable solution for smart homes, healthcare, and next-generation pervasive sensing applications.

Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: (i) intra-array coherent gain at the wavelength scale among antenna elements, and (ii) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays (URAs). These arrays are driven by multiplexing embedded radio frequency (RF) chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output (MIMO) systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy. WiCAL is validated using 3 × 4 URAs operating at the 5.2 GHz band. Experimental results demonstrate median AoA estimation errors of 1° in elevation and 1.5° in azimuth under intra-array coherent processing. For inter-array collaboration, the system achieves a median localization error of 15.6 cm using two URAs, outperforming state-of-the-art methods.

The reconfigurable intelligent surface (RIS) has garnered considerable attention due to its substantial potential in reconfiguring the electromagnetic environment. In RIS-aided communications, constrained ℓ2-norm maximization problems frequently arise due to the phase configuration requirements. This paper investigates a general discrete ℓp-norm maximization problem, with power aggregation through RIS as a specific example. We propose a mathematically concise iterative framework composed of alternating inner product maximizations, which is well-suited for addressing both ℓ1- and ℓ2-norm maximizations under either discrete or continuous uni-modular variable constraints. The iteration process is proven to be monotonically non-decreasing. Additionally, this framework exhibits a distinctive capability to mitigate performance degradation caused by discrete quantization in practical systems, which is applicable to any algorithm intended for the continuous solution. Furthermore, as an integral component of the alternating iterations framework, we present a divide-and-sort (DaS) method to tackle the discrete inner product maximization problem. In the realm of ℓ∞-norm maximization, the DaS method ensures the identification of the global optimum with polynomial search complexity. We validate the proposed methods’ effectiveness and superiority through numerical and prototype experiments. Finally, we demonstrate that the proposed framework can be extended and applied to a wide range of other engineering problems.

Reconfigurable intelligent surfaces (RISs) present significant potential for enhancing coverage and data rates in 6G wireless communication systems. While most research has focused on reflective RIS applications, studies on transmissive RIS (TRIS) have largely been limited to simulations or laboratory-scale prototypes. To evaluate the real-world performance of TRIS, we develop a 256-unit, 1-bit TRIS prototype operating at the 5.8 GHz frequency band. The design uses an antisymmetric configuration of two PIN diodes, achieving nearly uniform transmission amplitude with inverse phase states over a wide 20% bandwidth. A TRIS composed of 16 × 16 units is fabricated and validated through measurements, showing effective 1-bit phase tuning with minimal insertion loss and a 3 dB transmission bandwidth exceeding 1.2 GHz at the central frequency of 5.8 GHz. By dynamically modulating the quantized code distributions, ±90° scanning beams are achieved. We then integrate the TRIS into a wireless communication system and evaluate its performance in a real-world wall-penetration scenario. With directional antennas that are connected to the Universal Software Radio Peripheral (USRP) modules and are placed on either side of a 240 mm concrete wall, the TRIS provides a signal power gain of 19-23 dB within a ±90° beamforming range, significantly reducing path loss. Additionally, we assess its impact on a commercial Wi-Fi system, where the TRIS improves the average Signal-to-Noise Ratio (SNR) by 8 dB, with a maximum gain of 15 dB, across a 29 m2indoor area. Downlink rates also increase, with a maximum improvement of 10.57 Mbps and an average gain of 8.01 Mbps. These results highlight TRIS’s effectiveness in reducing path loss, enhancing SNR, and improving Wi-Fi performance in challenging wall-penetration environments, leading to better user experiences.

The existing methods for Reconfigurable Intelligent Surface (RIS) beamforming in wireless communication are typically limited to uniform phase quantization. However, in real world applications, the phase and bit resolution of RIS units are often non-uniform due to practical requirements and engineering challenges. To fill this research gap, we formulate an optimization problem for discrete non-uniform phase configuration in RIS assisted multiple-input single-output (MISO) communications. Subsequently, a partition-and-traversal (PAT) algorithm is proposed to solve that, achieving the global optimal solution. The efficacy and superiority of the PAT algorithm are validated through numerical simulations, and the impact of non-uniform phase quantization on system performance is analyzed.

This paper investigates a stacked intelligent metasurfaces (SIM)-assisted integrated sensing and communications (ISAC) system. An extended target model is considered, where the base station (BS) aims to estimate the complete target response matrix relative to the SIM. Under the constraints of minimum signal-to-interference-plus-noise ratio (SINR) for the communication users (CUs) and maximum transmit power, we jointly optimize the transmit beamforming at the BS and the end-to-end transmission matrix of the SIM, to minimize the Cramér-Rao bound (CRB) for target estimation. Effective algorithms such as alternating optimization (AO) and semidefinite relaxation (SDR) are employed to solve the non-convex SINR-constrained CRB minimization problem. Finally, we design and build a hardware platform for SIM, and experimentally evaluate the performance of SIM-aided communication and sensing tasks.

A fair beam allocation framework through reconfigurable intelligent surfaces (RISs) is proposed, incorporating the Max-min criterion. This framework focuses on explicit RIS beamforming functionalities through optimization. Firstly, realistic models, grounded in geometrical optics, are introduced to characterize the input/output behaviors of RISs. Then, a highly efficient algorithm is developed for Max-min optimizations involving quadratic forms. Leveraging the Moreau-Yosida approximation, we successfully reformulate the original problem and propose an iterative algorithm to obtain the optimal solution. The proposed approach exhibits excellent extensibility, making it readily applicable to address a broader class of Max-min optimization problems. Finally, numerical and prototype experiments are conducted to validate the effectiveness of the framework. With the proposed beam allocation framework and algorithm, we clarify that several crucial redistribution functionalities of RISs, such as explicit beam-splitting, fair beam allocation, and wide-beam generation, can be effectively implemented.

Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations caused by various environmental factors and the mobility of RISs. In this paper, we propose a novel modeling approach using Neural Radiance Fields (NeRF) to characterize the dynamics of electromagnetic fields in such environments. Our method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver. This two-stage process accurately characterizes multiple complex transmission paths, enhancing our understanding of signal behavior in real-world scenarios. Our approach predicts the signal field for any specified RIS placement and receiver location, facilitating efficient RIS deployment. Experimental evaluations using both simulated and real-world data validate the significant benefits of our methodology.

This paper explores the potential wireless communication applications of Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation environments. Unlike in free space, we utilize the sensitivity to boundaries of the enclosed electromagnetic (EM) field and the equivalent perturbation of RISs. For the first time, we introduce the framework of reconfigurable boundary modulation in the cavities. We have proposed a robust boundary modulation scheme that exploits the continuity of object motion and the mutation of the codebook switch, which achieves pulse position modulation (PPM) by RIS-generated equivalent pulses for wireless communication in cavities. This approach achieves around 2 Mbps bit rate in the prototype and demonstrates strong resistance to channel's frequency selectivity resulting in an extremely low bit error rate (BER).

Reconfigurable Intelligent Surface (RIS) is envisioned to be an enabling technique in 6G wireless communications. By configuring the reflection beamforming codebook, RIS focuses signals on target receivers to enhance signal strength. In this paper, we investigate the codebook configuration for RIS-aided communication systems. We formulate an implicit relationship between user's coordinates information and the codebook from the perspective of signal radiation mechanisms, and introduce a novel learning-based method, implicit neural representations (INRs), to solve this implicit coordinates-to-codebook mapping problem. Our approach requires only user's coordinates, avoiding reliance on channel models. Additionally, given the significant practical applications of the 1-bit RIS, we formulate the 1-bit codebook configuration as a multi-label classification problem, and propose an encoding strategy for 1-bit RIS to reduce the codebook dimension, thereby improving learning efficiency. Experimental results from simulations and measured data demonstrate significant advantages of our method.

Device-free human activity recognition plays a pivotal role in wireless sensing. However, current systems often fail to accommodate signal transmission through walls or necessitate dedicated noise removal algorithms. To overcome these limitations, we introduce TRTAR: a device-free passive human activity recognition system integrated with a transmissive reconfigurable intelligent surface (RIS). TRTAR eliminates the necessity for dedicated devices or noise removal algorithms, while specifically addressing signal propagation through walls. Unlike existing approaches, TRTAR solely employs a transmissive RIS at the transmitter or receiver without modifying the inherent hardware structure. Experimental results demonstrate that TRTAR attains an average accuracy of 98.13% when signals traverse concrete walls.

This paper studies the beamforming optimization challenge in reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) systems, where the RIS phase configuration is discrete. Conventional optimization meth-ods for this discrete optimization problem necessitate resource-intensive exponential search and thus fall within the universal (NP-hard) category. We formally define this task as a discrete inner product maximization problem. Leveraging the inherent structure of this problem, we propose an efficient divide-and-sort (Da$S$) search algorithm to reach the global optimality for the maximization problem. The complexity of the proposed algorithm can be minimized to$\mathrm{O}(2^{B}N)$, a linear correlation with the count of phase discrete levels$2^{B}$and reflecting units$N$. This is notably lower than the exhaustive search complexity of$\mathcal{O}(2^{BN})$. Numerical evaluations and experiments over real prototype also demonstrate the efficiency of the proposed DaS algorithm. Finally, by using the proposed algorithm, we show that over some resolution quantization level on each RIS unit (4-bit and above), there is no noticeable difference in power gains between continuous and discrete phase configurations.

Human activity recognition (HAR) is crucial in smart homes, security, and healthcare. Existing systems are limited by insufficient spatial diversity due to the constrained number of antennas. Additionally, challenges in noise reduction and feature extraction from sensing data, particularly channel state information (CSI), affect recognition performance. This study introduces a reconfigurable intelligent surface (RIS)-assisted passive HAR (RISAR) method compatible with commercial Wi-Fi devices. RISAR leverages RIS to enhance the spatial diversity of Wi-Fi signals, capturing a broader range of spatial information. A novel high-dimensional factor model based on random matrix theory is proposed to improve noise reduction and feature extraction in the temporal domain. Furthermore, a dual-stream spatiotemporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental results demonstrate that RISAR significantly outperforms existing HAR methods in both accuracy and efficiency, achieving an average accuracy of 97.26%. These findings highlight RISAR’s adaptability and potential as a robust activity recognition solution in real-world environments.

A fair beam allocation framework through reconfigurable intelligent surfaces (RISs) is proposed, incorporating the Max-min criterion. This framework focuses on designing explicit beamforming functionalities through optimization. Firstly, realistic models, grounded in geometrical optics, are introduced to characterize the input/output behaviors of RISs, effectively bridging the gap between the requirements on explicit beamforming operations and their practical implementations. Then, a highly efficient algorithm is developed for Max-min optimizations involving quadratic forms. Leveraging the Moreau-Yosida approximation, we successfully reformulate the original problem and propose an iterative algorithm to obtain the optimal solution. A comprehensive analysis of the algorithm’s convergence is provided. Importantly, this approach exhibits excellent extensibility, making it readily applicable to address a broader class of Max-min optimization problems. Finally, numerical and prototype experiments are conducted to validate the effectiveness of the framework. With the proposed beam allocation framework and algorithm, we clarify that several crucial redistribution functionalities of RISs, such as explicit beam-splitting, fair beam allocation, and wide-beam generation, can be effectively implemented. These explicit beamforming functionalities have not been thoroughly examined previously.

Physically accurate and mathematically tractable models are presented to characterize the scattering and reflection properties of reconfigurable intelligent surfaces (RISs). We describe a single metallic patch and patch array, as well as their interactions with multiple incident electromagnetic (EM) waves using continuous and discrete strategies. Our models take into account the effect of the incident and scattered angles, polarization features, and the topology and geometry of RISs. In particular, we propose a simple system of linear equations to characterize the multiple-input multiple-output (MIMO) behaviors of RISs under appropriate assumptions. This model can be used as a fundamental tool for analyzing and optimizing the performance of RIS-aided systems in the far-field regime. Using the proposed models, we identify the advantages and limitations of three typical configurations. An important discovery is that the popular phase compensation designs cannot provide complicated beam reshaping functionality. A possible solution is the simultaneous configurations of collecting area and phase shifting. Numerical simulations validate the effectiveness of the proposed configuration schemes.