Robust Beamforming Design for Integrated Sensing and Communication Systems: Considering Multi-Fold Uncertainty
Kai Yang, Jin Xu, Xiaofeng Tao, Mengying Sun, Huici Wu · IEEE Transactions on Cognitive Communications and Networking · 2025
By considering multiple uncertainties, including imperfect communication channel state information (CSI), target reflection coefficients, and clutter location, this paper investigates the enhanced robust beamforming design for integrated sensing and communication (ISAC) systems operating in complex environments. The dual-functional base station (BS) utilizes a receive filter to improve target detection performance. Our goal is to minimize power consumption at the BS by jointly optimizing the transmit beamforming and receive filter matrices, while satisfying the outage probability constraints for both communication rate and radar output signal-to-interference-plus-noise ratio (SINR). To address the formulated non-convex problem, an effective alternating optimization algorithm is proposed, leveraging Decomposition-Based Large Deviation Inequality (DBLDI), semidefinite relaxation (SDR), and generalized eigenvalue decomposition (GED) methods. Further, the robust design is extended to more complex scenarios by incorporating uncertainty in target location, where the worst-case radar SINR outage probability constraint is employed to ensure reliable sensing performance. Unlike most existing studies, we introduce a novel approach based on Cantelli’s Inequality (CI) to handle probabilistic constraints. To reduce computational complexity, a low-complexity method is developed within the constrained successive convex approximation (CSSCA) framework. Simulation results indicate that the proposed DBLDI and CI methods improve the feasibility rate by approximately 13%, compared to the baseline scheme. Moreover, the CSSCA method reduces computational complexity by approximately O(Nt3) relative to the DBLDI and CI methods.