Distributionally Robust Beamforming for Multicell ISAC: A Chance Constrained Optimization Approach

Chance-constrained optimization provides a powerful framework for decision-making under uncertainty and finds wide applications in areas. Conventional chance-constrained optimization assumes the transmission of the entire dataset to a single server, which may result in privacy breaches. In this paper, we propose a federated chance-constrained optimization approach for robust beamforming for multi-cell integrated sensing and communication systems. The proposed robust beamforming technique aims to minimize the worst-case base station transmission power while satisfying the chance constraint of communication rate with channel state information uncertainty as well as beampattern gain constraint of sensing target. Comprehensive simulation studies have been conducted to evaluate the efficacy of the proposed method and to corroborate the theoretical claims.