Optimized Design for IRS-Assisted Integrated Sensing and Communication Systems in Clutter Environments

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system design in a clutter environment. Assisted by an IRS equipped with a uniform planar array (UPA), a multi-antenna base station (BS) is targeted for simultaneously sensing multiple targets in the non-light-of-sight (NLoS) region and communicating with multiple communication users (CUs). We consider the joint IRS-assisted ISAC design in the case with Type-I or Type-II CUs, where each Type-I CU and Type-II CU can and cannot cancel the interference from sensing signals, respectively. Under the perfect communication/sensing channel state information assumption, we aim to maximize the minimum sensing beampattern gain among multiple targets, where the sensing beampattern gain qualifies the achieved illumination signal power at the given location of the target of interest. We jointly optimize the BS's communication-sensing beamformers and the IRS's phase shifting matrix, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each Type-I/Type-II CU, the interference power constraint per clutter, the transmission power constraint at the BS, and the cross-correlation pattern constraint. Due to the design variable coupling, the joint IRS-assisted ISAC design problem is shown to be non-convex in the case with Type-I or Type-II CUs. To circumvent the non-convexity dilemma, we propose semidefinite relaxation (SDR) based alternating optimization algorithms in both cases, for which the computational complexity and convergency behavior are analyzed. In the case with Type-I CUs, we show that the dedicated sensing signal at the BS can help enhance the sensing performance gain. By contrast, the dedicated sensing signal at the BS is not required for the IRS-assisted ISAC designs in the case with Type-II CUs. Numerical results are provided to show that the proposed IRS-assisted ISAC design schemes achieve a significant gain over the existing benchmark schemes.