We are pleased to announce that our paper, “ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities,” has been accepted for publication in IEEE Communications Surveys & Tutorials (Early Access). [: arxiv ]

In this work, the authors  —Mira Chandra Kirana, Patatchona Keyela, Fatemeh Rostamian,  Dr. Deemah H. Tashman, and Prof. Soumaya Cherkaoui — present a comprehensive survey of ML-enabled O-RAN, covering architectural foundations, learning frameworks, key challenges, and future research directions for 5G, 6G, and beyond.

While Open RAN is often discussed in terms of openness and disaggregation, its real potential lies in intelligence. In a follow-up insight post, we explore how machine learning reshapes O-RAN architectures, highlighting its role in enabling intelligent, adaptive, and secure network operations. We examine key challenges, clarify why ML is becoming indispensable in O-RAN, and discuss the research gaps and opportunities identified in our comprehensive survey.