ATHENA: Machine Learning and Reasoning for Radio Resources Scheduling in vRAN systems

• Published in: IEEE journal on selected areas in communications 2024-02, Vol.42 (2), p.1-1
• Main Authors: Apostolakis, Nikolaos, Gramaglia, Marco, Chatzieleftheriou, Livia Elena, Subramanya, Tejas, Banchs, Albert, Sanneck, Henning
• Format: Article
• Language: English
• Subjects: 3GPP; Algorithms; Artificial intelligence; Automation; Biological system modeling; Cognition; Computer architecture; Decision analysis; Deep Reinforcement Learning; Machine learning; Machine Reasoning; Radio access networks; Radio Resource Scheduling; Resource scheduling; Throughput; Virtual networks; vRAN
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2023.3336155

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 to understand why AI/ML algorithms made a decision, to be able to reason upon those decisions and, eventually, take further decisions related to e.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.