As wireless communication systems become more advanced, Open Radio Access Networks (O-RAN) stand out as a notable framework that promotes interoperability and cost-effectiveness. An examination of the progression of RAN architectures, as well as O-RAN's underlying principles, reveals the importance of machine learning (ML) in addressing various challenges, including spectrum management, resource allocation, and security. Hence, this survey provides a comprehensive overview of the integration of ML within O-RAN, highlighting its transformative potential in enhancing network performance and efficiency. This survey aims to describe the current status of ML applications in O-RAN while indicating possible directions for future research by analyzing existing literature. The findings aim to assist researchers and stakeholders in formulating optimal service strategies and advancing the understanding of intelligent wireless networks.
@article{arxiv.2604.01239,
title = {ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities},
author = {Mira Chandra Kirana and Patatchona Keyela and Fatemeh Rostamian and Deemah H. Tashman and Soumaya Cherkaoui},
journal= {arXiv preprint arXiv:2604.01239},
year = {2026}
}