Joint Deployment and Beamforming Design of Aerial STAR-RIS Aided Networks with Reinforcement Learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917350499942400 |
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| author | Ma, Zhuoyuan Zhao, Qi Zhang, Jin Yan, Bai |
| author_facet | Ma, Zhuoyuan Zhao, Qi Zhang, Jin Yan, Bai |
| contents | Aerial simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enables full-space coverage in dynamic wireless networks. However, most existing works assume fixed user grouping, overlooking the fact that STAR-RIS deployment inherently determines whether users are served via transmission or reflection. To address this, we propose a joint deployment and beamforming framework, where an aerial STAR-RIS dynamically adjusts its location and orientation to adaptively control user grouping and enhance hybrid beamforming. We formulate a Markov decision process (MDP) capturing the coupling among deployment, grouping, and signal design. To solve the resulting non-convex and time-varying problem, we develop a PPO-based reinforcement learning algorithm that adaptively balances user grouping and beamforming resources through online policy learning. Simulation results show 57.1\% and 285\% sum-rate gains over fixed-deployment and RIS-free baselines, respectively, demonstrating the benefit of user-grouping-aware control in STAR-RIS-aided systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_03520 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Joint Deployment and Beamforming Design of Aerial STAR-RIS Aided Networks with Reinforcement Learning Ma, Zhuoyuan Zhao, Qi Zhang, Jin Yan, Bai Systems and Control G.1.6, I.2.8 Aerial simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enables full-space coverage in dynamic wireless networks. However, most existing works assume fixed user grouping, overlooking the fact that STAR-RIS deployment inherently determines whether users are served via transmission or reflection. To address this, we propose a joint deployment and beamforming framework, where an aerial STAR-RIS dynamically adjusts its location and orientation to adaptively control user grouping and enhance hybrid beamforming. We formulate a Markov decision process (MDP) capturing the coupling among deployment, grouping, and signal design. To solve the resulting non-convex and time-varying problem, we develop a PPO-based reinforcement learning algorithm that adaptively balances user grouping and beamforming resources through online policy learning. Simulation results show 57.1\% and 285\% sum-rate gains over fixed-deployment and RIS-free baselines, respectively, demonstrating the benefit of user-grouping-aware control in STAR-RIS-aided systems. |
| title | Joint Deployment and Beamforming Design of Aerial STAR-RIS Aided Networks with Reinforcement Learning |
| topic | Systems and Control G.1.6, I.2.8 |
| url | https://arxiv.org/abs/2309.03520 |