Joint Antenna Position and Transmit Power Optimization for Pinching Antenna-Assisted ISAC Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909539127787520 |
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| author | Qin, Yunhui Fu, Yaru Zhang, Haijun |
| author_facet | Qin, Yunhui Fu, Yaru Zhang, Haijun |
| contents | This letter explores how pinching antennas, an advanced flexible-antenna system, can enhance the performance of integrated sensing and communication (ISAC) systems by leveraging their adaptability, cost-effectiveness, and ability to facilitate line-of-sight transmission. To achieve this, a joint antenna positioning and transmit power optimization problem is formulated to maximize the total communication data rate while meeting the target sensing requirements and the system energy constraint. To address the complex non-convex optimization problem, we propose a maximum entropy-based reinforcement learning (MERL) solution. By maximizing cumulative reward and policy entropy, this approach effectively balances exploration and exploitation to enhance robustness. Numerical results demonstrate that the proposed MERL algorithm surpasses other benchmark schemes in cumulative reward, total data rate, sensing signal-to-noise ratio, and stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Antenna Position and Transmit Power Optimization for Pinching Antenna-Assisted ISAC Systems Qin, Yunhui Fu, Yaru Zhang, Haijun Systems and Control This letter explores how pinching antennas, an advanced flexible-antenna system, can enhance the performance of integrated sensing and communication (ISAC) systems by leveraging their adaptability, cost-effectiveness, and ability to facilitate line-of-sight transmission. To achieve this, a joint antenna positioning and transmit power optimization problem is formulated to maximize the total communication data rate while meeting the target sensing requirements and the system energy constraint. To address the complex non-convex optimization problem, we propose a maximum entropy-based reinforcement learning (MERL) solution. By maximizing cumulative reward and policy entropy, this approach effectively balances exploration and exploitation to enhance robustness. Numerical results demonstrate that the proposed MERL algorithm surpasses other benchmark schemes in cumulative reward, total data rate, sensing signal-to-noise ratio, and stability. |
| title | Joint Antenna Position and Transmit Power Optimization for Pinching Antenna-Assisted ISAC Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.12872 |