RL based Beamforming Optimization for 3D Pinching Antenna assisted ISAC Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866917227698061312 |
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| author | Gao, Qian Zhong, Ruikang Liu, Yue Shin, Hyundong Liu, Yuanwei |
| author_facet | Gao, Qian Zhong, Ruikang Liu, Yue Shin, Hyundong Liu, Yuanwei |
| contents | In this paper, a three-dimensional (3D) deployment scheme of pinching antenna array is proposed, aiming to enhances the performance of integrated sensing and communication (ISAC) systems. To fully realize the potential of 3D deployment, a joint antenna positioning, time allocation and transmit power optimization problem is formulated to maximize the sum communication rate with the constraints of target sensing rates and system energy. To solve the sum rate maximization problem, we propose a heterogeneous graph neural network based reinforcement learning (HGRL) algorithm. Simulation results prove that 3D deployment of pinching antenna array outperforms 1D and 2D counterparts in ISAC systems. Moreover, the proposed HGRL algorithm surpasses other baselines in both performance and convergence speed due to the advanced observation construction of the environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RL based Beamforming Optimization for 3D Pinching Antenna assisted ISAC Systems Gao, Qian Zhong, Ruikang Liu, Yue Shin, Hyundong Liu, Yuanwei Signal Processing In this paper, a three-dimensional (3D) deployment scheme of pinching antenna array is proposed, aiming to enhances the performance of integrated sensing and communication (ISAC) systems. To fully realize the potential of 3D deployment, a joint antenna positioning, time allocation and transmit power optimization problem is formulated to maximize the sum communication rate with the constraints of target sensing rates and system energy. To solve the sum rate maximization problem, we propose a heterogeneous graph neural network based reinforcement learning (HGRL) algorithm. Simulation results prove that 3D deployment of pinching antenna array outperforms 1D and 2D counterparts in ISAC systems. Moreover, the proposed HGRL algorithm surpasses other baselines in both performance and convergence speed due to the advanced observation construction of the environment. |
| title | RL based Beamforming Optimization for 3D Pinching Antenna assisted ISAC Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.20654 |