Robust Beamforming Design for Integrated Sensing and Communication Systems

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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 multi-antenna 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 non-convex one that is challenging to solve. An iterative robust beamforming algorithm is designed to address this issue by employing S-procedure, semi-definite relaxation technique, Schur complementarity conditions, and successive convex approximation. Simulation results demonstrate that the proposed algorithm exhibits better convergence and stronger robustness.
Funding Information
  • National Natural Science Foundation of China (U23A20279, 62271094)
  • Natural Science Foundation of Chongqing Municipality (CSTB2022NSCQ, LZX0009, CSTB2023NSCQ-LZX0079)
  • Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K202200601)

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