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As an emerging wireless communication technology, reconfigurable intelligent surface (RIS) has become a basic choice for providing signal coverage services in scenarios with dense obstacles or long tunnels through multi-hop configurations. Conventional works of literature mainly focus on alternating optimization or single-beam calculation in RIS phase configuration, which is limited in considering energy efficiency, and often suffers from inaccurate channel state information (CSI), poor convergence, and high computational complexity. This paper addresses the design and optimization challenges for successive RIS-assisted multi-hop systems. Specifically, we establish a general model for multi-hop communication based on the relationship between the input and output electric fields within each RIS. Meanwhile, we derive the half-power beamwidth of the RIS-reflected beams, considering the beam direction. Leveraging these models and derivations, we propose deployment optimization and beam optimization strategies for multi-hop systems, which feature high aperture efficiency and significant gains in signal power. Simulation and prototype experiment results validate the effectiveness and superiority of the proposed systems and methods.

Passive arbitrary waveform generation (AWG) are especially important in a variety of fields like radar detection, wireless communications and integrated sensing and communications. Typically, backscatter devices are used to achieve passive signal reflection modulation to facilitate information transmission or to interfere with radar echoes. Reconfigurable Intelligent Surface (RIS) or Metasurface is a promising technology that combines the advantages of backscatter devices and reflective array antennas. Previous studies demonstrate diverse approaches to achieve reflection modulation by utilizing the superposition of the quantified reflective coefficient (RC) of each unit but suffer from the computing complexity of codebook sequence, the safety of communication, and the flexibility of modulation. To overcome the difficulties, we propose new paradigm of metasurface, i.e. AWG-RIS, that can independently generate arbitrary baseband waveforms and beam patterns based on a magnitude-phase decoupled unit design without altering the beam pattern. We proposed an analysis framework and introduce waveform factor and beamforming factor into the new model which provide the theoretical support for the flow from the control signal to the outgoing electromagnetic wave. Furthermore, we introduce the world's first prototype that demonstrates passive arbitrary waveform generation without altering the beam pattern. The experiments validate the generation of arbitrary waveforms and spectrograms, both for a single input and through the superposition of multiple inputs.

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 general discrete $\ell_p$-norm maximization problem, with the power enhancement at steering directions through reconfigurable intelligent surfaces (RISs) as an instance. We propose a mathematically concise iterative framework composed of alternating inner product maximizations, well-suited for addressing $\ell_1$- and $\ell_2$-norm maximizations with either discrete or continuous uni-modular variable constraints. The iteration is proven to be monotonically non-decreasing. Moreover, this framework exhibits a distinctive capability to mitigate performance degradation due to discrete quantization, establishing it as the first post-rounding lifting approach applicable to any algorithm intended for the continuous solution. Additionally, 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 $\ell_\infty$-norm maximization with discrete uni-modular constraints, the DaS ensures the identification of the global optimum with polynomial search complexity. We validate the effectiveness of the alternating inner product maximization framework in beamforming through RISs using both numerical experiments and field trials on prototypes. The results demonstrate that the proposed approach achieves higher power enhancement and outperforms other competitors. Finally, we show that discrete phase configurations with moderate quantization bits (e.g., 4-bit) exhibit comparable performance to continuous configurations in terms of power gains.

Human activity recognition (HAR) holds significant importance in smart homes, security, and healthcare. Existing systems face limitations because of the insufficient spatial diversity provided by a limited number of antennas. Furthermore, inefficiencies in noise reduction and feature extraction from sensing data pose challenges to recognition performance. This study presents a reconfigurable intelligent surface (RIS)-assisted passive human activity recognition (RISAR) method, compatible with commercial Wi-Fi devices. RISAR leverages a RIS to enhance the spatial diversity of Wi-Fi signals, effectively capturing a wider range of information distributed across the spatial domain. A novel high-dimensional factor model based on random matrix theory is proposed to address noise reduction and feature extraction in the temporal domain. A dual-stream spatial-temporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental analysis shows that RISAR significantly outperforms existing HAR methods in accuracy and efficiency, achieving an average accuracy of 97.26%. These findings underscore RISAR's adaptability and potential as a robust activity recognition solution in real environments.

Reconfigurable Intelligent Surfaces (RISs) exhibit promising enhancements in coverage and data rates for wireless communication systems, particularly in the context of 5G and beyond. This paper introduces a novel approach by focusing on the design and prototyping of a transmissive RIS, contrasting with existing research predominantly centered on reflective RIS. The achievement of 1-bit transmissive RIS through the antisymmetry configuration of the two PIN diodes, nearly uniform transmission magnitudes but inversed phase states in a wide band can be obtained. A transmissive RIS prototype consisting of 16 $\times$ 16 elements is meticulously designed, fabricated, and subjected to measurement to validate the proposed design. The results demonstrate that the proposed RIS unit cell achieves effective 1-bit phase tuning with minimal insertion loss and a transmission bandwidth of 3 dB exceeding $20\%$ at 5.8GHz. By dynamically modulating the quantized code distributions on the RIS, it becomes possible to construct scanning beams. The experimental outcomes of the RIS-assisted communication system validate that, in comparison to scenarios without RIS, the signal receiving power experiences an increase of approximately 7dB when RIS is deployed to overcome obstacles. This underscores the potential applicability of mobile RIS in practical communication.

In this paper, we propose a multi-RIS-aided wireless imaging framework in 3D facing the distributed placement of multi-sensor networks. The system creates a randomized reflection pattern by adjusting the RIS phase shift, enabling the receiver to capture signals within the designated space of interest (SoI). Firstly, a multi-RIS-aided linear imaging channel modeling is proposed. We introduce a theoretical framework of computational imaging to recover the signal strength distribution of the SOI. For the RIS-aided imaging system, the impact of multiple parameters on the performance of the imaging system is analyzed. The simulation results verify the correctness of the proposal. Furthermore, we propose an amplitude-only imaging algorithm for the RIS-aided imaging system to mitigate the problem of phase unpredictability. Finally, the performance verification of the imaging algorithm is carried out by proof of concept experiments under reasonable parameter settings.

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).

Passive sensing techniques based on Wi-Fi signals have emerged as a promising technology in advanced wireless communication systems due to their widespread application and cost-effectiveness. However, the proliferation of low-cost Internet of Things (IoT) devices has led to dense network deployments, resulting in increased levels of noise and interference in Wi-Fi environments. This, in turn, leads to noisy and redundant Channel State Information (CSI) data. As a consequence, the accuracy of human activity recognition based on Wi-Fi signals is compromised. To address this issue, we propose a novel CSI data signal extraction method. We established a human activity recognition system based on the Intel 5300 network interface cards (NICs) and collected a dataset containing six categories of human activities. Using our approach, signals extracted from the CSI data serve as inputs to machine learning (ML) classification algorithms to evaluate classification performance. In comparison to ML methods based on Principal Component Analysis (PCA), our proposed High-Dimensional Factor Model (HDFM) method improves recognition accuracy by 6.8%.

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 is \emph{independent} of 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., Comment: 21 pages

The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lies in balancing the number of beams used: it should be large enough to identify high-RSRP beams but small enough to minimize reporting overhead. This paper investigates this essential performance-versus-overhead trade-off using Bayesian optimization. The proposed approach represents the first application of real-time beam tracking via Bayesian optimization in RIS-assisted communication systems. Simulation results validate the effectiveness of this scheme.

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 iterations to attain 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 in the two rounds from the users to the server in 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. 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. In this paper we fully characterize of the the capacity region for this problem by proposing a new converse bound and an achievable scheme., Comment: 37 pages, 3 figures

Reconfigurable intelligent surfaces (RISs) have become one of the key technologies in 6G wireless communications. By configuring the reflection beamforming codebooks, RIS focuses signals on target receivers. In this paper, we investigate the codebook configuration for 1-bit RIS-aided systems. We propose a novel learning-based method built upon the advanced methodology of implicit neural representations. The proposed model learns a continuous and differentiable coordinate-to-codebook representation from samplings. Our method only requires the information of the user's coordinate and avoids the assumption of channel models. Moreover, we propose an encoding-decoding strategy to reduce the dimension of codebooks, and thus improve the learning efficiency of the proposed method. Experimental results on simulation and measured data demonstrated the remarkable advantages of the proposed method.

This paper addresses the hardware structure of Reconfigurable Intelligent Surfaces (RIS) and presents a comprehensive overview of RIS design, considering both unit design and prototype systems. It commences by tracing the evolutionary trajectory of RIS, originating from static cell-structured hypersurfaces. The article conducts a meticulous examination from the standpoint of adaptability, elucidating the diverse array of unit structures and design philosophies that underlie existing RIS frameworks. Following this, the study systematically categorizes and synthesizes channel modeling research for RIS-facilitated wireless communication, leveraging both physical insights and statistical data. Additionally, the article provides a detailed exposition of current RIS experimental setups and their corresponding empirical findings, delving into the attributes of prototype design and system functionalities. Moreover, this work introduces an in-house developed RIS prototype. The prototype undergoes rigorous empirical evaluation, encompassing multi-hop RIS signal amplification, image reconstruction, and real-world indoor signal coverage experiments. The empirical results robustly affirm the efficacy of RIS in effectively mitigating signal coverage blind spots and enabling radio wave imaging. With RIS-enhanced augmentation, the average indoor signal gain surpasses 8 dB.

The performance of multiple reconfigurable intelligent surfaces (RISs) receives limited attention in previous studies. This article fills this research gap by investigating the capabilities of multiple RISs in real-world networks. We propose a simplified yet highly scalable sandwich architecture for implementing one-bit unit cells, with the flexibility to accommodate multi-bit unit cells. To effectively control multiple RISs, we present a cost-effective remote-controlling scheme and develop a cloud-based RIS management system. Through a series of four field trials, we demonstrate the effectiveness of multi-hop routing schemes in establishing reliable links. Our experiments reveal significant improvements in signal strength and data transmission in multi-RIS-aided Wi-Fi and commercial 5G networks. Furthermore, we investigate the power scaling law of RIS-aided beamforming and provide insights into the roles of the later nodes in multi-hop relay chains.

This paper investigates the problem of maximizing the signal-to-noise ratio (SNR) in reconfigurable intelligent surface (RIS)-assisted MISO communication systems. The problem will be reformulated as a complex quadratic form problem with unit circle constraints. We proved that the SNR maximizing problem has a closed-form global optimal solution when it is a rank-one problem, whereas the former researchers regarded it as an optimization problem. Moreover, We propose a relaxation algorithm (RA) that relaxes the constraints to that of Rayleigh's quotient problem and then projects the solution back, where the SNR obtained by RA achieves much the same SNR as the upper bound but with significantly low time consumption. Then we asymptotically analyze its performance when the transmitter antennas n_t and the number of units of RIS N grow large together, with N/n_t -> c. Finally, our numerical simulations show that RA achieves over 98% of the performance of the upper bound and takes below 1% time consumption of manifold optimization (MO) and 0.1% of semidefinite relaxation (SDR).

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 methods 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 (DaS) search algorithm to reach the global optimality for the maximization problem. The complexity of the proposed algorithm can be minimized to $\mathcal{O}(2^BN)$, 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.

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 Cram\'er-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., Comment: 6 pages, 6 figures

In recent years, a brand-new technology, reconfigurable intelligent surface (RIS) has been widely studied for reconfiguring the wireless propagation environment. RIS is an artificial surface of electromagnetic material that is capable of customizing the propagation of the wave impinging upon it. Utilizing RIS for communication service like signal enhancement usually lead to non-convex optimization problems. Existing optimization methods either suffers from scalability issues for $N$ number of RIS elements large, or may lead to suboptimal solutions in some scenario. In this paper, we propose a divide-and-sort (DaS) discrete optimization approach, that is guaranteed to find the global optimal phase shifts for $1$-bit RIS, and has time complexity $\mathcal{O}(N \log(N))$. Numerical experiments show that the proposed approach achieves a better ``performance--complexity tradeoff'' over other methods for $1$-bit RIS.

Physically accurate and mathematically tractable models are presented to characterize scattering and reflection properties of reconfigurable intelligent surfaces (RISs). We take continuous and discrete strategies to model a single patch and patch array and their interactions with multiple incident electromagnetic (EM) waves. The proposed models consider the effect of the incident and scattered angles, polarization features, and the topology and geometry of RISs. Particularly, a simple system of linear equations can describe the multiple-input multiple-output (MIMO) behaviors of RISs under reasonable assumptions. It can serve as a fundamental model for analyzing and optimizing the performance of RIS-aided systems in the far-field regime. The proposed models are employed to identify the advantages and limitations of three typical configurations. One important finding is that complicated beam reshaping functionality can not be endowed by popular phase compensation configurations. A possible solution is the simultaneous configurations of collecting area and phase shifting. Numerical simulations verify the effectiveness of the proposed configuration schemes.

We investigate the asymptotics of eigenvalues of sample covariance matrices associated with a class of non-independent Gaussian processes (separable and temporally stationary) under the Kolmogorov asymptotic regime. The limiting spectral distribution (LSD) is shown to depend explicitly on the Kolmogorov constant (a fixed limiting ratio of the sample size to the dimensionality) and parameters representing the spatio- and temporal- correlations. The Cauchy, M- and N-transforms from free harmonic analysis play key roles to this LSD calculation problem. The free multiplication law of free random variables is employed to give a semi-closed-form expression (only the final step is numerical based) of the LSD for the spatio-covariance matrix being a diagonally dominant Wigner matrix and temporal-covariance matrix an exponential off-diagonal decay (Toeplitz) matrix. Furthermore, we also derive a nonlinear shrinkage estimator for the top eigenvalues associated with a low rank matrix (Hermitian) from its noisy measurements. Numerical studies about the effectiveness of the estimator are also presented.

Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Traditional weighted least square (WLS), as the most universal state estimation method, attempts to minimize the residual between measurements and the estimation of measured variables, but it is unable to handle the measurement error. To solve this problem, based on random matrix theory, this paper proposes a data-driven approach to clean measurement error in matrix-level. Our method significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS. In this method, a Hermitian matrix is constructed to establish an invertible relationship between the eigenvalues of measurements and their covariance matrix. Random matrix tools, combined with an optimization scheme, are used to clean measurement error by shrinking the eigenvalues of the covariance matrix. With great robustness and generality, our approach is particularly suitable for large interconnected power grids. Our method has been numerically evaluated using different testing systems, multiple models of measured noise and matrix size ratios., Comment: has been accepted by IEEE trans on Power Systems

With the deployment of online monitoring systems in distribution networks, massive amounts of data collected through them contains rich information on the operating states of the networks. By leveraging the data, an unsupervised approach based on bidirectional generative adversarial networks (BiGANs) is proposed for operational risk assessment in distribution networks in this paper. The approach includes two stages: (1) adversarial feature learning. The most representative features are extracted from the online monitoring data and a statistical index $\mathcal{N}_{\phi}$ is calculated for the features, during which we make no assumptions or simplifications on the real data. (2) operational risk assessment. The confidence level $1-\alpha$ for the population mean of the standardized $\mathcal{N}_{\phi}$ is combined with the operational risk levels which are divided into emergency, high risk, preventive and normal, and the p value for each data point is calculated and compared with $\frac{\alpha}{2}$ to determine the risk levels. The proposed approach is capable of discovering the latent structure of the real data and providing more accurate assessment result. The synthetic data is employed to illustrate the selection of parameters involved in the proposed approach. Case studies on the real-world online monitoring data validate the effectiveness and advantages of the proposed approach in risk assessment., Comment: 10 pages, IEEE Trans on Power Systems, submitted

We provide another framework of iterative algorithms based on thresholding, feedback and null space tuning for sparse signal recovery arising in sparse representations and compressed sensing. Several thresholding algorithms with various feedbacks are derived, which are seen as exceedingly effective and fast. Convergence results are also provided. The core algorithm is shown to converge in finite many steps under a (preconditioned) restricted isometry condition. The algorithms are seen as particularly effective for large scale problems. Numerical studies about the effectiveness and the speed of the algorithms are also presented.

Signals with sparse frame representations comprise a much more realistic model of nature than that with orthonomal bases. Studies about the signal recovery associated with such sparsity models have been one of major focuses in compressed sensing. In such settings, one important and widely used signal recovery approach is known as $\ell_1$-synthesis (or Basis Pursuit). We present in this article a more effective performance analysis (than what are available) of this approach in which the dictionary $\Dbf$ may be highly, and even perfectly correlated. Under suitable conditions on the sensing matrix $\Phibf$, an error bound of the recovered signal $\hat{\fbf}$ (by the $\ell_1$-synthesis method) is established. Such an error bound is governed by the decaying property of $\tilde{\Dbf}_{\text{o}}^*\fbf$, where $\fbf$ is the true signal and $\tilde{\Dbf}_{\text{o}}$ denotes the optimal dual frame of $\Dbf$ in the sense that $\|\tilde{\Dbf}_{\text{o}}^*\hat{\fbf}\|_1$ produces the smallest $\|\tilde{\Dbf}^*\tilde{\fbf}\|_1$ in value among all dual frames $\tilde{\Dbf}$ of $\Dbf$ and all feasible signals $\tilde{\fbf}$. This new performance analysis departs from the usual description of the combo $\Phibf\Dbf$, and places the description on $\Phibf$. Examples are demonstrated to show that when the usual analysis fails to explain the working performance of the synthesis approach, the newly established results do., Comment: 19 pages, 7 figures

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 $\ell_1$-analysis. We expand in this article 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 alternative 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 analysis operators. An iterative algorithm is outlined for solving the optimal-dual-based $\ell_1$-analysis problem. The effectiveness of the proposed method and algorithm is demonstrated through several experiments., Comment: 34 pages, 8 figures. To appear in IEEE Transactions on Information Theory