Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912569055248384 |
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| author | Zhang, Zeyuan Xiu, Yue Dong, Zheng Yin, Jiacheng Khabbaz, Maurice J. Assi, Chadi Wei, Ning |
| author_facet | Zhang, Zeyuan Xiu, Yue Dong, Zheng Yin, Jiacheng Khabbaz, Maurice J. Assi, Chadi Wei, Ning |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication Zhang, Zeyuan Xiu, Yue Dong, Zheng Yin, Jiacheng Khabbaz, Maurice J. Assi, Chadi Wei, Ning Signal Processing 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. |
| title | Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.03273 |