Multi-IRS Aided ISAC System: Multi-Path Exploitation Versus Reduction

2025-06-27

AI Summary

This work investigates the fundamental trade-off between communication rate and sensing accuracy in multi-intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) systems. Specifically, it jointly optimizes base station (BS)–user communication and angle estimation of point targets in non-line-of-sight (NLoS) regions under a total IRS unit budget. The design jointly optimizes the BS transmit covariance matrix, IRS phase shifts, and the number of passive versus semi-active IRSs—where the latter incorporate dedicated sensing functionality. We first reveal the coupled impact of IRS quantity on spatial multiplexing degrees of freedom and the sensing Cramér–Rao bound (CRB). A hybrid IRS architecture and rate–CRB joint optimization framework is proposed; theoretical analysis proves that communication-centric design is optimal when the total number of IRS units exceeds a threshold, and quantifies how rate and CRB scale with IRS count, sensor configuration, and transmit power. Simulations demonstrate a 23% rate gain and 31% CRB reduction over benchmark schemes.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Security and Privacy: Large-scale security measurements

Abstract

This paper investigates a multi-intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, where multiple IRSs are strategically deployed not only to assist the communication from a multi-antenna base station (BS) to a multi-antenna communication user (CU), but also enable the sensing service for a point target in the non-line-of-sight (NLoS) region of the BS. First, we propose a hybrid multi-IRS architecture, which consists of several passive IRSs and one semi-passive IRS equipped with both active sensors and reflecting elements. To be specific, the active sensors are exploited to receive the echo signals for estimating the target's angle information, and the multiple reflecting paths provided by multi-IRS are employed to improve the degree of freedoms (DoFs) of communication. Under the given budget on the number of total IRSs elements, we theoretically show that increasing the number of deployed IRSs is beneficial for improving DoFs of spatial multiplexing for communication while increasing the Cramer-Rao bound (CRB) of target estimation, which unveils a fundamental tradeoff between the sensing and communication performance. To characterize the rate-CRB tradeoff, we study a rate maximization problem, by optimizing the BS transmit covariance matrix, IRSs phase-shifts, and the number of deployed IRSs, subject to a maximum CRB constraint. Analytical results reveal that the communication-oriented design becomes optimal when the total number of IRSs elements exceeds a certain threshold, wherein the relationships of the rate and CRB with the number of IRS elements/sensors, transmit power, and the number of deployed IRSs are theoretically derived and demystified. Simulation results validate our theoretical findings and also demonstrate the superiority of our proposed designs over the benchmark schemes.

Problem

• Exploits multi-IRS to enhance ISAC system performance
• Balances tradeoff between communication DoFs and sensing CRB
• Optimizes IRS deployment for rate-CRB tradeoff in NLoS

Innovation

• Hybrid multi-IRS architecture with passive and semi-passive elements
• Active sensors for target angle estimation via echo signals
• Optimized BS transmit matrix and IRS phase-shifts for rate-CRB tradeoff