Bayesian Beamforming for Integrated Sensing and Communication Systems

TL;DR

This paper introduces a Bayesian beamforming approach for integrated sensing and communication systems that models target uncertainty and optimizes detection probability, demonstrating improved robustness and performance over benchmarks.

Contribution

It proposes a novel Bayesian beamforming scheme utilizing expected detection probability and develops an SCA-SDR algorithm for non-convex optimization in ISAC systems.

Findings

Outperforms benchmark beamforming schemes

Exhibits robust detection with unknown target parameters

Optimizes detection probability under power and communication constraints

Abstract

The uncertainty of the sensing target brings great challenge to the beamforming design of the integrated sensing and communication (ISAC) system. To address this issue, we model the scattering coefficient and azimuth angle of the target as random variables and introduce a novel metric, expected detection probability (EPd), to quantify the average detection performance from a Bayesian perspective. Furthermore, we design a Bayesian beamforming scheme to optimize the expected detection probability under the limited power budget and communication performance constraints. A successive convex approximation and semidefinite relaxation-based (SCA-SDR) algorithm is developed for the complicated non-convex optimization problem corresponding to the beamforming scheme. Simulation results show that the proposed scheme outperforms other benchmarks and exhibits robust detection performance when parameters of the target are unknown and random.