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

• Published in: IEEE transactions on wireless communications Vol. 21; no. 12; pp. 10351 - 10365
• Main Authors: Huang, Zhe, Wang, Kexuan, Liu, An, Cai, Yunlong, Du, Rui, Han, Tony Xiao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayesian analysis; Beamforming; Channel estimation; Communications systems; Integrated sensing and communication; Object detection; Optimization; pilot design; Pilots; Radar; Radar detection; Radar targets; Sensors; sparse Bayesian inference; Sparsity; Statistical inference; Target detection; Uplink
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3183621

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. In Stage 2, the BS exploits the prior information obtained in Stage 1 to optimize the DP signal to 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 (MM) method. Simulations verify the advantages of the proposed scheme.