ATHENA: Machine Learning and Reasoning for Radio Resources Scheduling in vRAN Systems
Nikolaos Apostolakis, Marco Gramaglia, Livia Elena Chatzieleftheriou, Tejas Subramanya, Albert Banchs, Henning Sanneck · IEEE Journal on Selected Areas in Communications · 2023
Next-generation mobile networks will rely on their autonomous operation. Virtual Network Functions empowered by Artificial Intelligence (AI) and Machine Learning (ML) can adapt to varying environments that encompass both network conditions and the cloud platform executing them. In this view, it becomes paramount tounderstand whyAI/ML algorithms made a decision, to be able to reason upon those decisions and, eventually, take further decisions related toe.g., network orchestration. In this paper, we present ATHENA, an ML-based radio resource scheduler for virtualized Radio Access Network (RAN) system. Our real-software implementation shows that the proposed ML-based approach can outperform the baseline solution. We discuss how additional re-orchestration actions can be taken by analyzing our scheduling decisions and learning from the past.