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 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.