Joint Pilot Optimization, Target Detection and Channel Estimation for Integrated Sensing and Communication Systems

TL;DR

This paper presents a two-stage joint pilot optimization, target detection, and channel estimation scheme for integrated sensing and communication systems, exploiting joint burst sparsity to improve performance in 6G applications.

Contribution

It introduces a novel two-stage scheme with a Turbo Sparse Bayesian inference algorithm and an efficient pilot optimization method for integrated sensing and communication systems.

Findings

Enhanced target detection and channel estimation accuracy.

Effective exploitation of joint burst sparsity improves system performance.

Simulation results confirm the advantages of the proposed scheme.

Abstract

Radar sensing will be integrated into the 6G communication system to support various applications. In this integrated sensing and communication system, a radar target may also be a communication channel scatterer. In this case, the radar and communication channels exhibit certain joint burst sparsity. We propose a two-stage joint pilot optimization, target detection and channel estimation scheme to exploit such joint burst sparsity and pilot beamforming gain to enhance detection/estimation performance. In Stage 1, the base station (BS) sends downlink pilots (DP) for initial target search, and the user sends uplink pilots (UP) for channel estimation. Then the BS performs joint target detection and channel estimation based on the reflected DP and received UP signals. In Stage 2, the BS exploits the prior information obtained in Stage 1 to optimize the DP signal to achieve beamforming gain and further refine the performance. A Turbo Sparse Bayesian inference algorithm is proposed for joint target detection and channel estimation in both stages. The pilot optimization problem in Stage 2 is a semi-definite programming with rank-1 constraints. By replacing the rank-1 constraint with a tight and smooth approximation, we propose an efficient pilot optimization algorithm based on the majorization-minimization method. Simulations verify the advantages of the proposed scheme.