Recent Research on Reinforcement Learning for Open RAN
Heejae Park, Kihyun Seol, Seungyeop Song, Yerin Lee, Laihyuk Park · 2024
Open Radio Access Network (O-RAN) has been proposed as a flexible, interoperable framework that enhances innovation for network vendors and operators. However, as O-RAN deployments expand, challenges such as dynamic resource management and real-time decision-making emerge. Recent studies have explored integrating Reinforcement Learning (RL) to address these issues. This paper analyzes current research trends in applying RL within the O-RAN framework to provide insights for future developments.