Joint Target Detection and Channel Estimation for Distributed Massive MIMO ISAC Systems

Integrated sensing and communication (ISAC) can be integrated into the beyond 5G (B5G) and 6G communication systems to achieve high-resolution radar sensing with wireless communication signals. Joint target detection and channel estimation are essential for various emerging applications in massive MIMO ISAC systems. Inspired by the idea that partial radar targets could also be the communication scatterers, in this paper, we consider the joint target detection and multi-user downlink channel estimation problem for the massive MIMO ISAC systems, where the potential common sparsity among the sensing and multi-user communication channels is exploited to enhance the ISAC detection/estimation performance. In order to decrease communication overhead and computational complexity, we initially introduce a distributed scheme for joint target detection and multi-user downlink channel estimation. Subsequently, we propose a novel sparse Bayesian learning (SBL) framework that concurrently characterizes common and individual sparsity of signals-of-interest. Moreover, a fast hybrid message passing algorithm with distributed processing is devised for joint target detection and channel estimation. Compared with the existing methods, the proposed method has lower communication overhead and computational complexity, and can be applied to the more challenging multi-user downlink channel estimation task. Simulation results are provided to verify the efficiency of the proposed method.