Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks

TL;DR

This paper introduces a novel deep reinforcement learning framework to optimize UAV trajectory, RIS phase shifts, and NOMA power control in CoMP-NOMA networks, significantly enhancing spectral efficiency and coverage.

Contribution

We develop a multi-output proximal policy optimization algorithm to jointly optimize hybrid parameters in ARIS-assisted CoMP-NOMA networks, addressing a complex, hybrid optimization problem.

Findings

MO-PPO achieves near-optimal performance in simulations.

The framework adapts effectively to dynamic environments.

Integration of ARIS improves spectral efficiency and coverage.

Abstract

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks.