DRL-based Joint Beamforming and Power Allocation in Beyond Diagonal Reconfigurable Intelligence Surface 6G Systems
Year: 2025
Type: article
Abstract: 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.
Authors: Mousa Abdollahvand, Sima Sobhi-givi
Institution: University of Mohaghegh Ardabili
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7009204438
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