Distributed and Multi-Task Learning at the Edge for Energy Efficient Radio Access Networks

The big data availability of Radio Access Network (RAN) statistics suggests using it for improving the network management through machine learning based Self Organized Network (SON) functionalities. However, this may increase the already high energy consumption of mobile networks. Multiaccess Edge Computing can mitigate this problem; however, the machine learning solutions have to be properly designed for efficiently working in a distributed fashion. In this work, we propose distributed architectures for two RAN SON functionalities based on multi-task and gossip learning. We evaluate their accuracy and consumed energy in realistic scenarios. Results show that the proposed distributed implementations have the same performance but save energy with respect to their correspondent centralized versions and benchmark solutions. We conclude the paper discussing open research issues for this interesting emerging field.Grant numbers : 5G-REFINE - Resource EfFIcient 5G NEtworks (TEC2017-88373-R).© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works