{"ID":2875016,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03273","arxiv_id":"2509.03273","title":"Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.03273","url_pdf":"https://arxiv.org/pdf/2509.03273v1","authors":"[\"Zeyuan Zhang\",\"Yue Xiu\",\"Zheng Dong\",\"Jiacheng Yin\",\"Maurice J. Khabbaz\",\"Chadi Assi\",\"Ning Wei\"]","published":"2025-09-03T12:50:53Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false}