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.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight (LOS) links and reflect the uplink pilot signal to sensors. To overcome the mutually coupled problem between the beamforming design at the RIS and DoA estimation, we explore the separable sparse representation structure and propose an alternating optimization algorithm. The grid-based DoA estimation is modeled as a joint-sparse recovery problem considering the grid bias, and the Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The corresponding Cramér-Rao lower bound (CRLB) is derived to evaluate the estimation. Then, the beampattern at the RIS is optimized to maximize the signal-to-noise (SNR) at sensors according to the estimated angles. Numerical results show that the proposed alternating optimization algorithm can achieve lower estimation error compared to benchmarks of random beamforming design.

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.

Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when deployed on new devices, might struggle to generalize well due to domain shifts. In this context, federated domain adaptation (FDA) emerges as a powerful approach to address this challenge. Most existing FDA approaches typically focus on aligning the distributions between source and target domains by minimizing their (e.g., MMD) distance. Such strategies, however, inevitably introduce high communication overheads and can be highly sensitive to network reliability. In this paper, we introduce RF-TCA, an enhancement to the standard Transfer Component Analysis approach that significantly accelerates computation without compromising theoretical and empirical performance. Leveraging the computational advantage of RF-TCA, we further extend it to FDA setting with FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that isindependentof the sample size, while maintaining performance that is either comparable to or even surpasses state-of-the-art FDA methods. We present extensive experiments to showcase the superior performance and robustness (to network condition) of FedRF-TCA.

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR.

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.

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.

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.

This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a server aggregates the trained models without retrieving other information about users' local data. Secure aggregation generally contains two phases, namely key sharing phase and model aggregation phase. Due to the common effect of user dropouts in federated learning, the model aggregation phase should contain two rounds, where in the first round the users transmit masked models and according to the identity of surviving users, the surviving users then transmit some further messages to help the server decrypt the sum of users' trained models. The objective of the considered information theoretic formulation is to characterize the capacity region of the communication rates from the users to the server in the two rounds of the model aggregation phase, by assuming that the key sharing have already been done offline in prior. If the keys shared by the users could be any random variables, the capacity was fully characterized in the literature. Recently, an additional constraint on the keys (referred to as uncoded groupwise keys) was added into the problem, where there are several independent keys in the system and each key is shared by exactly S users, where S is a system parameter. In this paper, we fully characterize the capacity region for this problem by matching new converse and achievable bounds.

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.

This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a curious-but-honest server aggregates the trained models without retrieving other information about users’ local data. Secure aggregation generally contains two phases, namely key sharing phase and model aggregation phase. Due to the common effect of user dropouts in federated learning, the model aggregation phase should contain two rounds, where in the first round the users transmit masked models and, in the second round, according to the identity of surviving users after the first round, these surviving users transmit some further messages to help the server decrypt the sum of users’ trained models. The objective of the considered information theoretic formulation is to characterize the capacity region of the communication rates from the users to the server in the two rounds of the model aggregation phase, assuming that key sharing has already been performed offline in prior. In this context, Zhao and Sun completely characterized the capacity region under the assumption that the keys can be arbitrary random variables. More recently, an additional constraint, known as “uncoded groupwise keys,” has been introduced. This constraint entails the presence of multiple independent keys within the system, with each key being shared by precisely S users, where S is a defined system parameter. The capacity region for the information theoretic secure aggregation problem with uncoded groupwise keys was established in our recent work subject to the condition S > K - U, where K is the number of total users and U is the designed minimum number of surviving users (which is another system parameter). In this paper we fully characterize the capacity region for this problem by matching a new converse bound and an achievable scheme. Experimental results over the Tencent Cloud show the improvement on the model aggregation time compared to the original secure aggregation scheme.

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.

Remaining useful life (RUL) prediction is of fundamental importance in reliability analysis and health diagnosis of complex industrial systems. Aiming at improving the prediction accuracy, this article proposes a normalizing flow embedded sequence-to-sequence (seq2seq) learning method to predict the RUL of an asset or a system. This method introduces a block of normalizing flow into the middle area of the familiar encoder–decoder structure of the seq2seq model. This normalizing flow enjoys the remarkable representation ability for the nonlinearity between input sequential data and outputs and enables the original seq2seq model to be more suitable for vibration signals of engines. The encoder and the decoder, which fall before and after the normalizing flow, are both built by gated recurrent units. Besides, a one-hot coding of clustering is concatenated with measurement data to indicate the frequently shifting vibration state, and a sensor selection method is designed to drop some weakly related and ineffective variables. Our method is tested and further analyzed by 2008 IEEE PHM challenge data (PHM08), of which many practical preprocessing methods are conducted. Numerous tests verify that our method outperforms other related deep learning methods for RUL estimation.

Signals with sparse representations in frames comprise a much more realistic model of nature, it is therefore highly desirable to extend the compressed sensing methodology to redundant dictionaries (or frames) as opposed to orthonormal bases only. In the generalized setting, the standard approach to recover the signal is known as ℓ1-synthesis (or Basis Pursuit). In this paper, we present the performance analysis of this approach in which the dictionary may be highly - and even perfectly - correlated. Our results do not depend on an accurate recovery of the coefficients. We demonstrate the validity of the results via several experiments.

A sparse-dual-frame based ℓ1-analysis approach for compressed sensing (CS) is proposed. The sparse dual frame is a notion of optimal dual frames of a non-exact frame. It is motivated in the study of compressed sensing problems where signals are sparse with respect to redundant dictionaries (frames). An alternating iterative algorithm is proposed. An error bound ensuring the correct signal recovery is obtained. Empirical studies over generally difficult CS problems demonstrate that the new sparse-dual-based approach provides satisfactory solutions, whereas other existing means may not.

Compressed sensing with sparse frame representations is seen to have much greater range of practical applications than that with orthonormal bases. In such settings, one approach to recover the signal is known as ℓ1-analysis. We expand in this paper the performance analysis of this approach by providing a weaker recovery condition than existing results in the literature. Our analysis is also broadly based on general frames and alter native dual frames (as analysis operators). As one application to such a general-dual-based approach and performance analysis, an optimal-dual-based technique is proposed to demonstrate the effectiveness of using alternative dual frames as ℓ1-analysis operators. An iterative algorithm is outlined for solving the optimal-dual-based -analysis problem. The effectiveness of the proposed method and algorithm is demonstrated through several experiments.