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arXiv · 2509.03273

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

Zeyuan Zhang·Yue Xiu·Zheng Dong·Jiacheng Yin·Maurice J. Khabbaz·Chadi Assi·Ning Wei
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Abstract

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 the MA scenario. Then, a Cramer-Rao bound (CRB) minimization problem driven by joint beamforming and antenna position design is presented. Specifically, to address this highly non-convex flexible beamforming problem, we deploy a deep reinforcement learning (DRL) approach to train a flexible beamforming agent. To ensure stability during training, a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted to balance exploration with reward maximization for efficient and reliable learning. Numerical results demonstrate that the proposed crosstalk-resilient (CR) algorithm enhances the overall ISAC performance compared to other benchmark schemes.

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Zeyuan Zhang, Yue Xiu, Zheng Dong, Jiacheng Yin, Maurice J. Khabbaz, Chadi Assi, Ning Wei. 2025-09-03. Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication. https://arxiv.org/abs/2509.03273

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