Faulty RIS-aided Integrated Sensing and Communication: Modeling and Optimization

TL;DR

This paper studies the impact of faulty RIS elements on integrated sensing and communication systems, deriving bounds and proposing an optimization method to mitigate performance degradation caused by these faults.

Contribution

It introduces a novel modeling framework for faulty RIS in ISAC systems, deriving the MCRB and developing a joint optimization algorithm to improve system robustness.

Findings

Proposed method reduces MCRB loss by 21.25% on average.

Performance gain increases with more faulty elements.

New modeling approach for faulty RIS in ISAC systems.

Abstract

This work investigates a practical reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where a subset of RIS elements fail to function properly and reflect incident signals randomly towards unintended directions, thereby degrading system performance. To date, no study has addressed such impairments caused by faulty RIS elements in ISAC systems. This work aims to fill the gap. First, to quantify the impact of faulty elements on ISAC performance, we derive the misspecified Cram\'er-Rao bound (MCRB) for sensing parameter estimation and signal-to-interference-and-noise ratio (SINR) for communication quality. Then, to mitigate the performance loss caused by faulty elements, we jointly design the remaining functional RIS phase shifts and transmit beamforming to minimize the MCRB, subject to the communication SINR and transmit power constraints. The resulting optimization problem is highly non-convex due to the intricate structure of the MCRB expression and constant-modulus constraint imposed on RIS. To address this, we reformulate it into a more tractable form and propose a block coordinate descent (BCD) algorithm that incorporates majorization-minimization (MM), successive convex approximation (SCA), and penalization techniques. Simulation results demonstrate that our proposed approach reduces the MCRB performance loss by 21.25% on average compared to the case where the presence of faulty elements is ignored. Furthermore, the performance gain becomes more evident as the number of faulty elements increases.