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

2026-03-27

AI Summary

This study addresses the challenges of open radio access networks (O-RAN) in spectrum management, resource allocation, and security by providing a systematic review of the current state and future trajectory of integrating machine learning (ML) with O-RAN. By synthesizing O-RAN architectural specifications, representative ML algorithms, and integration strategies, this work establishes the first comprehensive analytical framework encompassing application scenarios, technical architectures, and open research questions. The analysis delineates key directions for ML-enabled O-RAN innovation, offering both theoretical foundations and practical guidance to advance the efficient evolution of intelligent wireless networks for academia and industry alike.

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.

Problem

• Open RAN
• machine learning
• spectrum management
• resource allocation
• security

Innovation

• Machine Learning
• Open RAN
• Resource Allocation
• Spectrum Management
• Intelligent Wireless Networks