DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems
This paper introduces a new method for improving wireless communication systems by employing beyond diagonal reconfigurable intelligent surfaces (BD-RIS) and unmanned aerial vehicle (UAV) alongside deep reinforcement learning (DRL) techniques. BD-RIS represents a departure from traditional RIS desig...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2025-03-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3301-en.pdf |
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| author | Mousa Abdollahvand Sima Sobhi-givi |
| author_facet | Mousa Abdollahvand Sima Sobhi-givi |
| author_sort | Mousa Abdollahvand |
| collection | DOAJ |
| description | This paper introduces a new method for improving wireless communication systems by employing beyond diagonal reconfigurable intelligent surfaces (BD-RIS) and unmanned aerial vehicle (UAV) alongside deep reinforcement learning (DRL) techniques. BD-RIS represents a departure from traditional RIS designs, providing advanced capabilities for manipulating electromagnetic waves to optimize the performance of communication. We propose a DRL-based framework for optimizing the UAV and configuration of BD-RIS elements, including hybrid beamforming, phase shift adjustments, and transmit power coefficients for non-orthogonal multiple access (NOMA) transmission by considering max-min fairness. Through extensive simulations and performance evaluations, we demonstrate that BD-RIS outperforms conventional RIS architectures. Additionally, we analyze the convergence speed and performance trade-offs of different DRL algorithms, emphasizing the importance of selecting the appropriate algorithm and hyper-parameters for specific applications. Our findings underscore the transformative potential of BD-RIS and DRL in enhancing wireless communication systems, laying the groundwork for next-generation network optimization and deployment. |
| format | Article |
| id | doaj-art-aa8d520ac96f44a888671bdccdf0f77a |
| institution | DOAJ |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-aa8d520ac96f44a888671bdccdf0f77a2025-08-20T02:48:12ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902025-03-0121133013301DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G SystemsMousa Abdollahvand0Sima Sobhi-givi1 Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran This paper introduces a new method for improving wireless communication systems by employing beyond diagonal reconfigurable intelligent surfaces (BD-RIS) and unmanned aerial vehicle (UAV) alongside deep reinforcement learning (DRL) techniques. BD-RIS represents a departure from traditional RIS designs, providing advanced capabilities for manipulating electromagnetic waves to optimize the performance of communication. We propose a DRL-based framework for optimizing the UAV and configuration of BD-RIS elements, including hybrid beamforming, phase shift adjustments, and transmit power coefficients for non-orthogonal multiple access (NOMA) transmission by considering max-min fairness. Through extensive simulations and performance evaluations, we demonstrate that BD-RIS outperforms conventional RIS architectures. Additionally, we analyze the convergence speed and performance trade-offs of different DRL algorithms, emphasizing the importance of selecting the appropriate algorithm and hyper-parameters for specific applications. Our findings underscore the transformative potential of BD-RIS and DRL in enhancing wireless communication systems, laying the groundwork for next-generation network optimization and deployment.http://ijeee.iust.ac.ir/article-1-3301-en.pdfunmanned aerial vehicle (uav)beyond diagonal-reconfigurable intelligent surface (bd-ris)non-orthogonal multiple access (noma)hybrid beamformingreinforcement learning (rl). |
| spellingShingle | Mousa Abdollahvand Sima Sobhi-givi DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems Iranian Journal of Electrical and Electronic Engineering unmanned aerial vehicle (uav) beyond diagonal-reconfigurable intelligent surface (bd-ris) non-orthogonal multiple access (noma) hybrid beamforming reinforcement learning (rl). |
| title | DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems |
| title_full | DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems |
| title_fullStr | DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems |
| title_full_unstemmed | DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems |
| title_short | DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems |
| title_sort | drl based joint beamforming and power allocation in beyond diagonal reconfigurable intelligence surface 6g systems |
| topic | unmanned aerial vehicle (uav) beyond diagonal-reconfigurable intelligent surface (bd-ris) non-orthogonal multiple access (noma) hybrid beamforming reinforcement learning (rl). |
| url | http://ijeee.iust.ac.ir/article-1-3301-en.pdf |
| work_keys_str_mv | AT mousaabdollahvand drlbasedjointbeamformingandpowerallocationinbeyonddiagonalreconfigurableintelligencesurface6gsystems AT simasobhigivi drlbasedjointbeamformingandpowerallocationinbeyonddiagonalreconfigurableintelligencesurface6gsystems |