Robust Energy-Efficient DRL-Based Optimization in UAV-Mounted RIS Systems with Jitter
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911170781249536 |
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| author | Salim, Mahmoud M. Rabie, Khaled M. Muqaibel, Ali H. |
| author_facet | Salim, Mahmoud M. Rabie, Khaled M. Muqaibel, Ali H. |
| contents | In this letter, we propose an energy-efficient design for an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS) communication system with nonlinear energy harvesting (EH) and UAV jitter. A joint optimization problem is formulated to maximize the EH efficiency of the UAV-mounted RIS by controlling the user powers, RIS phase shifts, and time-switching factor, subject to quality of service and practical EH constraints. The problem is nonconvex and time-coupled due to UAV angular jitter and nonlinear EH dynamics, making it intractable for conventional optimization methods. To address this, we reformulate the problem as a deep reinforcement learning (DRL) environment and develop a smoothed softmax dual deep deterministic policy gradient algorithm. The proposed method incorporates action clipping, entropy regularization, and softmax-weighted Q-value estimation to improve learning stability and exploration. Simulation results show that the proposed algorithm converges reliably under various UAV jitter levels and achieves an average EH efficiency of 45.07\%, approaching the 53.09\% upper bound of exhaustive search, and outperforming other DRL baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17971 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Energy-Efficient DRL-Based Optimization in UAV-Mounted RIS Systems with Jitter Salim, Mahmoud M. Rabie, Khaled M. Muqaibel, Ali H. Information Theory Signal Processing In this letter, we propose an energy-efficient design for an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS) communication system with nonlinear energy harvesting (EH) and UAV jitter. A joint optimization problem is formulated to maximize the EH efficiency of the UAV-mounted RIS by controlling the user powers, RIS phase shifts, and time-switching factor, subject to quality of service and practical EH constraints. The problem is nonconvex and time-coupled due to UAV angular jitter and nonlinear EH dynamics, making it intractable for conventional optimization methods. To address this, we reformulate the problem as a deep reinforcement learning (DRL) environment and develop a smoothed softmax dual deep deterministic policy gradient algorithm. The proposed method incorporates action clipping, entropy regularization, and softmax-weighted Q-value estimation to improve learning stability and exploration. Simulation results show that the proposed algorithm converges reliably under various UAV jitter levels and achieves an average EH efficiency of 45.07\%, approaching the 53.09\% upper bound of exhaustive search, and outperforming other DRL baselines. |
| title | Robust Energy-Efficient DRL-Based Optimization in UAV-Mounted RIS Systems with Jitter |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2506.17971 |