Destructive and constructive RIS beamforming in an ISAC-multi-user MIMO network

TL;DR

This work investigates RIS-enabled ISAC in MU-MIMO networks, addressing both constructive and destructive beamforming. It develops an alternating-optimization framework that couples precoder design with RIS phase-shift updates to maximize the sensing SNR $\rho$ under per-user SINR constraints and power limits, using semidefinite relaxation and MM-based phase optimization, along with a CCP-based method for a malicious RIS attack that minimizes $\rho$ while preserving UE SINR. The study also incorporates a clustered RIS pixel-failure model to assess robustness. Numerical results show that RISs can equally boost or degrade system performance, and hardware impairments or attacks significantly influence achievable sensing gains, underscoring security and reliability considerations for RIS-enabled ISAC systems.

Abstract

Integrated sensing and communication (ISAC) has already established itself as a promising solution to the spectrum scarcity problem, even more so when paired with a reconfigurable intelligent surface (RIS), as RISs can shape the propagation environment by adjusting their phase-shift coefficients. Albeit the potential performance gain, a RIS is also a potential security threat to the system. In this paper, we explore both the positive and negative sides of having a RIS in a multi-user multiple-input multiple-output (MIMO) ISAC network. We first develop an alternating optimization algorithm, obtaining the active and passive beamforming vectors that maximize the sensing signal-to-noise ratio (SNR) under minimum signal-to-interference-plus-noise ratio (SINR) constraints for the communication users and finite power budget. We also investigate the destructive potential of the RIS by devising a RIS phase-shift optimization algorithm that minimizes the sensing SNR while preserving the same minimum communication SINR previously guaranteed by the system. We further investigate the impact of the RIS's individual element failures on the system performance. The simulation results show that the RIS performance-boosting potential is as good as its destructive one and that both of our optimization strategies are hindered by the investigated impairments.

Figures (6)

• Figure 1: A RIS-aided ISAC network where a hacker has hacked into the RIS's control circuit.
• Figure 2: Illustration of the simulation setup.
• Figure 3: Alg. 1 $\rho$ maximization vs $t$ for different values of $N$.
• Figure 4: Alg. 1 $\rho$ maximization vs $t$ in presence of failures.
• Figure 5: Alg. 2 $\rho$ minimization vs $t$ for different values of $\gamma$.