Communication-Efficient and Scalable Management of Self-Organizing Mobile Networks

The diverse Self-Organizing Network (SON) Functions (SFs) targeted for the different network goals, while executing concurrently, tend to conflict with the operation of each other. It is possible to train data-driven models for SON self-coordination that can capture the complex dynamics between the simultaneously operating SFs. Standardized functions like Management Data Analytics Service (MDAS) would enable several solution providers to participate in the SON ecosystem but the high cost of data-off loading along with privacy concerns render it unfavourable for the mobile network operators. In this work, we propose a Federated Learning (FL) enabled G-SHOCC (Generic engine for Self-Healing, self-optimization and self-Coordination in Cellular networks) framework that provides a communication-efficient and scalable platform to generate high-quality models for SON without the need for transferring raw network data. We train a Deep Neural Network (DNN) model using the FL-enabled G-SHOCC framework to achieve implicit coordination between three intertwined SON functions – CCO, ICIC and COC, and evaluate its performance in terms of coverage and capacity during normal and faulty network scenarios. Finally, we demonstrate that the overall parameter recommendations and the closed-loop performance of the FL model are comparable to the centrally trained version of the DNN model.