Recent Advances in Machine Learning for Network Automation in the O-RAN

Area of Science:

• Telecommunications Engineering
• Computer Science
• Artificial Intelligence

Background:

• The telecommunications industry is shifting towards open and intelligent network architectures.
• Open Radio Access Network (O-RAN) architecture offers disaggregation and virtualization for multi-vendor interoperability.
• Managing and automating the complex O-RAN ecosystem poses significant challenges.

Purpose of the Study:

• To provide a comprehensive survey of current research on network automation using Machine Learning (ML) in O-RAN.
• To highlight the need for automation within the O-RAN architecture.
• To explore O-RAN's support for ML techniques and identify research opportunities.

Main Methods:

• Overview of the O-RAN architecture and its components.
• Analysis of O-RAN's inherent support for ML techniques.
• Exploration of challenges in applying ML for O-RAN automation.
• Review of existing research on ML algorithms and frameworks for O-RAN automation.

Main Results:

• Identified ML as a promising solution for automating complex O-RAN environments.
• Detailed current research efforts, including ML algorithms and frameworks applied to O-RAN.
• Highlighted key challenges and opportunities for ML-driven network automation in O-RAN.

Conclusions:

• ML techniques are crucial for addressing the complexities of O-RAN network automation.
• The survey provides a roadmap for future research in leveraging ML for intelligent O-RAN management.
• Further research can unlock significant benefits by applying ML to various aspects of O-RAN.