Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication

This paper investigates a movable antenna (MA)-enabled integrated sensing and communication (ISAC) system under the influence of antenna crosstalk. First, it generalizes the antenna crosstalk model from the conventional fixed-position antenna (FPA) system to MA. Then, a Cramer-Rao bound (CRB) minimization problem driven by joint beamforming and antenna position design is presented. To address the problem, a deep reinforcement learning (DRL) approach, specifically the twin delayed deep deterministic policy gradient (TD3) algorithm is employed to train a flexible beamforming agent. Numerical results show that the proposed crosstalk-resilient (CR) beamforming outperforms other benchmark schemes under antenna crosstalk.