ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities

TL;DR

This survey reviews how machine learning is integrated into Open RAN architectures, emphasizing its potential to improve network performance, address challenges, and guide future research in intelligent wireless networks.

Contribution

It provides a comprehensive overview of ML applications in O-RAN, highlighting current status, challenges, and future research directions.

Findings

ML enhances spectrum management and resource allocation in O-RAN.

ML integration improves security and network efficiency.

The survey identifies key challenges and opportunities for ML in O-RAN.

Abstract

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.