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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran University of Science and Technology
2025-03-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/article-1-3301-en.pdf |
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| Summary: | 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. |
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| ISSN: | 1735-2827 2383-3890 |