Recent Studies on Deep Reinforcement Learning in RIS-UAV Communication Networks
Tri‐Hai Nguyen, Heejae Park, Laihyuk Park
Abstract
Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies have recently been identified as enablers for future wireless networks. Deep reinforcement learning (DRL) is also a potential technique for optimizing performance in dynamic and complex networking environments. In this paper, we examine the state-of-the-art studies on DRL utilization in RIS-UAV communication systems concerning their objectives, optimization parameters, deployment scenarios, and DRL methods. In addition, we emphasize research challenges and directions that can be addressed to improve RIS-UAV networks.
Topics & Concepts
Reinforcement learningSoftware deploymentComputer scienceWirelessState (computer science)Artificial intelligenceDistributed computingSystems engineeringReal-time computingTelecommunicationsEngineeringSoftware engineeringAlgorithmAdvanced Wireless Communication TechnologiesUAV Applications and OptimizationEnergy Harvesting in Wireless Networks