SON coordination for parameter conflict resolution: A reinforcement learning framework

Self Organizing Network (SON) functions are meant to automate the network tuning, providing responses to the network state evolution. An instance of a SON function can run on one cell (distributed architecture) or can be built to govern a cluster of cells (centralized/hybrid architecture). From the operator point of view, SON functions are seen as black boxes. Several independent instances of one or multiple SON functions running in parallel are likely to generate conflicts and unstable network behavior. At a higher level, the SON-COordinator (SONCO) seeks to solve these conflicts. This paper addresses the design of a SONCO. We focus on coordinating two distributed SON functions: Mobility Load Balancing (MLB) and Mobility Robustness Optimization (MRO). Thus on each cell we will have an MLB and an MRO instance. The MLB instances will tune the Cell Individual Offset (CIO) parameter and the MRO instances will tune the HandOver (HO) Hysteresis parameter together with the CIO parameter. The task of the SONCO is to solve the conflicts that will appear on the CIO parameter. We propose a Reinforcement Learning (RL) framework as it offers the possibility to improve the decisions based on past experiences. We outline the tradeoff between configurations through numeric results.