Deep Learning-Based Resource Allocation in UAV-RIS-Aided Cell-Free Hybrid NOMA/OMA Networks
GLOBECOM 2023 - 2023 IEEE Global Communications Conference(2023)
Abstract
This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients, and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.
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Key words
Reconfigurable intelligent surface,unmanned aerial vehicle,reinforcement learning,deep learning
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