@inproceedings{52640538444f497b95a943b5ea483867,
title = "Recent Research on Reinforcement Learning for Open RAN",
abstract = "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.",
keywords = "O-RAN, optimization, recent research, RL",
author = "Heejae Park and Kihyun Seol and Seungyeop Song and Yerin Lee and Laihyuk Park",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th International Conference on Information and Communication Technology Convergence, ICTC 2024 ; Conference date: 16-10-2024 Through 18-10-2024",
year = "2024",
doi = "10.1109/ICTC62082.2024.10827667",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "1744--1745",
booktitle = "ICTC 2024 - 15th International Conference on ICT Convergence",
}