Robust Beamforming Design for Integrated Sensing and Communication Systems

Abstract

Integrated sensing and communication (ISAC) can improve spectral, energy, and transmission efficiency. To overcome the impact of channel uncertainties, we investigate a robust beamforming design problem for a multiple-input single-output based ISAC system with imperfect channel state information (CSI), where a multiantenna base station (BS) serves multiple wireless users and obtains state information of a point target. Based on bounded CSI error models, a total throughput maximization problem is formulated under the constraints of the minimum rate threshold of each communication user, sensing performance based on Cramér–Rao lower bound thresholds, and the maximum transmit power of the BS. The formulated problem with parameter perturbations belongs to a nonconvex one that is challenging to solve. To address this complexity, an iterative robust beamforming algorithm is designed by employing S-procedure, semidefinite relaxation technique, Schur complementarity conditions, and successive convex approximation. Simulation results demonstrate that the proposed algorithm exhibits better convergence and stronger robustness.