Edge-distributed Coordinated Hyper-Parameter Search for Energy Saving SON Use-Case

Energy Efficient operation of ultra-dense hetero-geneous network deployments is a big challenge for mobile networks. AI-assisted energy saving is one of the potential self-organizing network use cases for radio access network intelli-gence that can be used to predict the service load. This prediction can in turn be leveraged for proactively turning OFF/ON the capacity booster small cells within the coverage of always ON macro cells. These ML workloads can reside in macro cell base stations as opposed to conventional cloud-centric architecture to meet beyond 5G ambitious requirements of ultra-low latency, highest reliability, and scalability. However, the power-hungry hyperparameter search of ML workloads distributed at edges of the radio access network is a major challenge that can have substantial effect on the overall energy -efficiency of the network. In this paper, we illustrate how coordinated efficient training of distributed edge- ML models driven energy saving functions can enhance network energy efficiency. We validate the proposed method through a data-driven simulation methodology augmenting real traffic traces and comparing it with variants of legacy edge-ML hyper-parameter search techniques.