skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on June 8, 2026

Title: Beyond Diagonal RIS Aided Power Minimization Beamforming for MIMO Systems
Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with interconnected reflecting elements present an emerging technology for manipulating the propagation environment, and their new structure requires careful investigation. In this paper, we explore BD-RIS-aided power minimization beamforming, where the BD-RIS scattering matrix and transmit beamforming are jointly optimized under nonconvex constraints related to signal-to-interference-plus-noise ratio (SINR) thresholds and the structure of the scattering matrix. To efficiently solve the problem, we propose a single-loop algorithm, where we adopt a variable splitting strategy with an auxiliary variable to split the scattering matrix, and then alternately update the resulting variables. Through further derivations, we show that each nonconvex subproblem can be solved efficiently. Simulation results demonstrate the high efficiency of our proposed single loop algorithm and the effectiveness of BD-RIS in improving performance.  more » « less
Award ID(s):
2030029 2107182
PAR ID:
10638601
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
IEEE
Date Published:
Page Range / eLocation ID:
1821 to 1826
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. While reconfigurable intelligent surface (RIS) technology shows great promise for wireless communication, an adversary using such technology can threaten wireless performance. This paper explores an RIS-based attack on time-division duplex (TDD) based wireless systems that use channel reciprocity for physical layer key generation (PLKG). We demonstrate that deploying a non-reciprocal RIS with a non-symmetric "beyond diagonal" (BD) phase shift matrix can compromise channel reciprocity and thus break key consistency. The attack can be achieved without transmission of signal energy, channel state information (CSI), and synchronization with the legitimate system, and thus it is difficult to detect and counteract. We propose a physically consistent BD-RIS model and verify the impact of its attack on the secret key rate (SKR) of the legitimate system via simulations. Moreover, we provide a heuristic approach for optimizing the BD-RIS configuration to realize a more severe attack in cases where some partial knowledge of the channel state information is available. Our results demonstrate that such channel reciprocity attacks can significantly decrease the SKR of the legitimate system. 
    more » « less
  2. Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To over-come this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural net-work (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16 × 2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity. 
    more » « less
  3. In this paper, we investigate the potential of employing reconfigurable intelligent surface (RIS) in integrated sensing and communication (ISAC) systems. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communication and target detection. We aim to jointly design the transmit beamforming and receive filter of the BS, and the reflection coefficients of the RIS to maximize the sum-rate of the communication users, while satisfying a worst-case radar output signal-to-noise ratio (SNR), the transmit power constraint, and the unit modulus property of the reflecting coefficients. An efficient iterative algorithm based on fractional programming (FP), majorization-minimization (MM), and alternative direction method of multipliers (ADMM) is developed to solve the complicated non-convex problem. Simulation results verify the advantage of the proposed RIS-assisted ISAC scheme and the effectiveness of the developed algorithm. 
    more » « less
  4. We propose a novel graph neural network (GNN) architecture for jointly optimizing user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase shift in a multi-RIS aided multi-cell network. The proposed architecture represents BSs and users as nodes in a bipartite graph where the same type of nodes shares the same neural networks for generating messages and updating its representations, allowing for distributed implementation. In addition, we utilize a composite reflected channel estimation integrated between layers of the GNN structure to significantly reduce the signaling overhead and complexity required for channel estimation in a multi-RIS network. To avoid BS overload, load balancing is regularized in the training of the GNN and we further develop a collision avoidance algorithm to ensure strict load balancing at every BS. Numerical results show that the proposed GNN architecture is significantly more efficient than existing approaches. The results further demonstrate its strong scalability with network size and achieving a throughput performance approaching that of a centralized traditional optimization algorithm, without requiring individual RIS-reflected channels estimation and without the need for re-training or fine-tuning. 
    more » « less
  5. This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: 1) spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; 2) spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; 3) physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications. 
    more » « less