Beyond Diagonal RIS for ISAC Network: Statistical Analysis and Network Parameter Estimation

TL;DR

This paper provides a comprehensive statistical analysis of BD-RIS in ISAC networks, deriving closed-form CDFs for radar SNR and communication SINR, and introduces an SNIS algorithm for network parameter estimation under outage constraints.

Contribution

It introduces tractable closed-form CDFs for radar SNR and communication SINR in BD-RIS-assisted ISAC, and proposes an SNIS algorithm for network parameter estimation.

Findings

Derived accurate closed-form CDFs for SNR and SINR.

Demonstrated the effectiveness of the SNIS algorithm in meeting outage constraints.

Numerical results validate the analytical models and proposed methods.

Abstract

This paper investigates the use of beyond diagonal reconfigurable intelligent surface (BD-RIS) with $N$ elements to advance integrated sensing and communication (ISAC). We address a key gap in the statistical characterizations of the radar signal-to-noise ratio (SNR) and the communication signal-to-interference-plus-noise ratio (SINR) by deriving tractable closed-form cumulative distribution functions (CDFs) for these metrics. Our approach maximizes the radar SNR by jointly configuring radar beamforming and BD-RIS phase shifts. Subsequently, zero-forcing is adopted to mitigate user interference, enhancing the communication SINR. To meet ISAC outage requirements, we propose an analytically-driven successive non-inversion sampling (SNIS) algorithm for estimating network parameters satisfying network outage constraints. Numerical results illustrate the accuracy of the derived CDFs and demonstrate the effectiveness of the proposed SNIS algorithm.