Channel Estimation via Joint Angle-Delay Sparse Structure in ISAC Scenarios

In the upcoming 6G communications, radar sensing will merge with communication to support diverse application scenarios. However, within complex Integrated Sensing and Communication (ISAC) channels, radar targets and communication scatterers may overlap. Sensing and communication channels can exhibit similar sparse distributions in both the angle and delay domains. The increasing adoption of channel sparsity in communication parameter estimation has led to the development of numerous Compressed Sensing (CS) algorithms, many of which are based on the Expectation Maximization (EM) method. However, most EM-based algorithms depend on specific scenarios for designing channel sparse structures. Therefore, there is a pressing need to develop suitable channel sparse structures and tailor CS algorithms specifically for ISAC scenarios. This paper addresses the multiple-input multiple-output ISAC channel estimation issue using a bi-static model. We propose a Joint Angle-Delay Sparse Structure (JADSS) to capture the sparse characteristics of ISAC channels in both angle and delay domains. Additionally, we devise a message passing estimator to estimate the ISAC channel. Simulation demonstrate that the proposed CS algorithm achieves competitive error performance.