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

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 also poses a security threat to the system: in this paper, we explore both sides of the RIS presence in a multi-user MIMO (multiple-input multiple-output) ISAC network. We first develop an alternating optimization algorithm, obtaining the active and passive beamforming vectors maximizing 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 RIS by devising a RIS phase-shift optimization algorithm that minimizes 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 performances. The simulation results show that the RIS performance-boosting potential is as good as its destructive one and that both of our optimization strategies show some resilience towards the investigated impairments.

Malicious Reconfigurable Intelligent Surfaces: How Impactful can Destructive Beamforming be?

Reconfigurable intelligent surfaces (RISs) have demonstrated significant potential for enhancing communication system performance if properly configured. However, a RIS might also pose a risk to the network security. In this letter, we explore the impact of a malicious RIS on a multi-user multiple-input single-output (MISO) system when the system is unaware of the RIS's malicious intentions. The objective of the malicious RIS is to degrade the \ac{SNR} of a specific \ac{UE}, with the option of preserving the SNR of the other UEs, making the attack harder to detect. To achieve this goal, we derive the optimal RIS phase-shift pattern, assuming perfect channel state information (CSI) at the hacker. We then relax this assumption by introducing CSI uncertainties and subsequently determine the RIS's phase-shift pattern using a robust optimization approach. Our simulations reveal a direct proportionality between the performance degradation caused by the malicious RIS and the number of reflective elements, along with resilience toward CSI uncertainties.