ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities
For researchers and stakeholders in wireless networks, this survey provides a structured overview of ML in O-RAN, but it is incremental as it does not introduce new results or benchmarks.
This survey reviews the integration of machine learning in Open RAN, covering architectures, challenges, and opportunities in spectrum management, resource allocation, and security. It synthesizes existing literature to guide future research and strategy formulation.
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.