Reconfigurable Intelligent Surface-Assisted Aerial Nonterrestrial Networks: An Intelligent Synergy With Deep Reinforcement Learning

Umer, Muhammad ORCID: https://orcid.org/0009-0001-8751-6100, Mohsin, Muhammad Ahmed ORCID: https://orcid.org/0009-0005-2766-0345, Kaushik, Aryan ORCID: https://orcid.org/0000-0001-6252-4641, Nadeem, Qurrat-Ul-Ain ORCID: https://orcid.org/0000-0001-8423-3482, Nasir, Ali Arshad ORCID: https://orcid.org/0000-0001-5012-1562 and Hassan, Syed Ali ORCID: https://orcid.org/0000-0002-8572-7377 (2025) Reconfigurable Intelligent Surface-Assisted Aerial Nonterrestrial Networks: An Intelligent Synergy With Deep Reinforcement Learning. IEEE Vehicular Technology Magazine. ISSN 1556-6080

Preview

Accepted Version Available under License Creative Commons Attribution. Download (697kB) | Preview

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 (CoMPNOMA) 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 nextgeneration wireless networks.