Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization
Uncrewed aerial vehicles (UAVs) have emerged as a promising solution for enhancing wireless networks, especially in challenging environments. However, recent studies that integrate reconfigurable intelligent surfaces (RIS) with UAVs tend to focus on limited aspects, such as single-UAV deployments or...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/11081475/ |
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| author | Alif Rahmatullah Umar Hasan Albinsaid Chia-Po Wei Chih-Peng Li |
| author_facet | Alif Rahmatullah Umar Hasan Albinsaid Chia-Po Wei Chih-Peng Li |
| author_sort | Alif Rahmatullah Umar |
| collection | DOAJ |
| description | Uncrewed aerial vehicles (UAVs) have emerged as a promising solution for enhancing wireless networks, especially in challenging environments. However, recent studies that integrate reconfigurable intelligent surfaces (RIS) with UAVs tend to focus on limited aspects, such as single-UAV deployments or partial optimization of system parameters, thereby neglecting a comprehensive system-level design. To overcome these limitations, we propose a multi-user MISO communication network that leverages RIS-assisted UAVs to maximize both sum-rate and energy efficiency as two distinct objectives. Our approach stands out by considering multiple UAVs and incorporating four critical constraints: UAV flying areas, power limitations, transmit beamforming, and RIS requirements. We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). By jointly optimizing UAV coordinates, the transmit beamforming matrix, and RIS phase shifts, our method achieves significant performance improvements under dynamic environmental conditions. Extensive simulations show that our comprehensive strategy delivers higher sum-rates and enhanced energy efficiency, underscoring its practical potential for next-generation RIS-assisted UAV communication systems. |
| format | Article |
| id | doaj-art-2b07ae2734bc4bcfb94c340c1c082d6c |
| institution | DOAJ |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-2b07ae2734bc4bcfb94c340c1c082d6c2025-08-20T02:46:40ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0162033204710.1109/OJVT.2025.358966111081475Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency MaximizationAlif Rahmatullah Umar0https://orcid.org/0000-0002-3906-6803Hasan Albinsaid1https://orcid.org/0000-0001-9316-1839Chia-Po Wei2https://orcid.org/0000-0002-9266-1628Chih-Peng Li3https://orcid.org/0000-0003-0050-0921Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung, TaiwanElectrical and Computer Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaDepartment of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, TaiwanInstitute of Communications Engineering, National Sun Yat-sen University, Kaohsiung, TaiwanUncrewed aerial vehicles (UAVs) have emerged as a promising solution for enhancing wireless networks, especially in challenging environments. However, recent studies that integrate reconfigurable intelligent surfaces (RIS) with UAVs tend to focus on limited aspects, such as single-UAV deployments or partial optimization of system parameters, thereby neglecting a comprehensive system-level design. To overcome these limitations, we propose a multi-user MISO communication network that leverages RIS-assisted UAVs to maximize both sum-rate and energy efficiency as two distinct objectives. Our approach stands out by considering multiple UAVs and incorporating four critical constraints: UAV flying areas, power limitations, transmit beamforming, and RIS requirements. We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). By jointly optimizing UAV coordinates, the transmit beamforming matrix, and RIS phase shifts, our method achieves significant performance improvements under dynamic environmental conditions. Extensive simulations show that our comprehensive strategy delivers higher sum-rates and enhanced energy efficiency, underscoring its practical potential for next-generation RIS-assisted UAV communication systems.https://ieeexplore.ieee.org/document/11081475/Deep reinforcement learningmulti-UAV communicationmulti-user multiple input single outputreconfigurable intelligent surface |
| spellingShingle | Alif Rahmatullah Umar Hasan Albinsaid Chia-Po Wei Chih-Peng Li Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization IEEE Open Journal of Vehicular Technology Deep reinforcement learning multi-UAV communication multi-user multiple input single output reconfigurable intelligent surface |
| title | Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization |
| title_full | Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization |
| title_fullStr | Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization |
| title_full_unstemmed | Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization |
| title_short | Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization |
| title_sort | deep reinforcement learning for ris assisted multi uav mu miso communication networks sum rate and energy efficiency maximization |
| topic | Deep reinforcement learning multi-UAV communication multi-user multiple input single output reconfigurable intelligent surface |
| url | https://ieeexplore.ieee.org/document/11081475/ |
| work_keys_str_mv | AT alifrahmatullahumar deepreinforcementlearningforrisassistedmultiuavmumisocommunicationnetworkssumrateandenergyefficiencymaximization AT hasanalbinsaid deepreinforcementlearningforrisassistedmultiuavmumisocommunicationnetworkssumrateandenergyefficiencymaximization AT chiapowei deepreinforcementlearningforrisassistedmultiuavmumisocommunicationnetworkssumrateandenergyefficiencymaximization AT chihpengli deepreinforcementlearningforrisassistedmultiuavmumisocommunicationnetworkssumrateandenergyefficiencymaximization |