RIS-Assisted Aerial Non-Terrestrial Networks: An Intelligent Synergy with Deep Reinforcement Learning

Abstract

Reconfigurable intelligent surface (RIS)-assisted aerial non-terrestrial networks (NTNs) offer a promising paradigm for enhancing wireless communications in the era of 6G and beyond. By integrating RIS with aerial platforms such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs), these networks can intelligently control signal propagation, extending coverage, improving capacity, and enhancing link reliability. This article explores the application of deep reinforcement learning (DRL) as a powerful tool for optimizing RIS-assisted aerial NTNs. We focus on hybrid proximal policy optimization (H-PPO), a robust DRL algorithm well-suited for handling the complex, hybrid action spaces inherent in these networks. Through a case study of an aerial RIS (ARIS)-aided coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) network, we demonstrate how H-PPO can effectively optimize the system and maximize the sum rate while adhering to system constraints. Finally, we discuss key challenges and promising research directions for DRL-powered RIS-assisted aerial NTNs, highlighting their potential to transform next-generation wireless networks.

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