Joint Target Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly improve the channel estimation (CE) and target/scatterer/user sensing performance in the considered system, we propose a two-phase transmission scheme, where the coarse and refined CE/sensing results are respectively obtained in the first and second phases. Particularly, in each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is devised to solve the considered joint sensing and CE problem. The proposed algorithm incorporates the partial overlapping structured (POS) sparsity between the sensing and communication channels to improve the performance. Simulation results are provided to verify the superiority of the proposed algorithm.