Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910681884786688 |
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| author | Umer, Muhammad Mohsin, Muhammad Ahmed Mahmood, Aamir Dev, Kapal Jung, Haejoon Gidlund, Mikael Hassan, Syed Ali |
| author_facet | Umer, Muhammad Mohsin, Muhammad Ahmed Mahmood, Aamir Dev, Kapal Jung, Haejoon Gidlund, Mikael Hassan, Syed Ali |
| contents | This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01338 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks Umer, Muhammad Mohsin, Muhammad Ahmed Mahmood, Aamir Dev, Kapal Jung, Haejoon Gidlund, Mikael Hassan, Syed Ali Signal Processing This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks. |
| title | Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.01338 |