Published November 22, 2023 | Version v1
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ATHENA: Machine Learning and Reasoning for Radio Resources Scheduling in vRAN systems

Description

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.

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Related works

Is previous version of
Journal article: 10.1109/JSAC.2023.3336155 (DOI)

Funding

DAEMON – Network intelligence for aDAptive and sElf-Learning MObile Networks 101017109
European Commission
TrialsNet – TRials supported by Smart Networks beyond 5G 101095871
European Commission