Low complexity SON coordination using reinforcement learning

Abstract

The continuously increasing traffic demand faces us with increased CAPital Expenditures (CAPEX) and Operational Expenditure (OPEX). Self Organizing Network (SON) functions aim to lower these costs by automating the network tuning. A SON instance is a realization of a SON function which can tune one or a set of cells. Having several uncoordinated SON functions in the network creates a risk for conflicts and instability. This raises the need for a SON Coordinator (SONCO) meant to deal with these issues. In this work we consider that on each cell we have one SON instance of each SON function. We present the design of a SONCO which arbitrates conflicts based on weights attributed to the SON functions. The design makes use of Reinforcement Learning (RL) with function approximation. We provide a low complexity approximation of the action-value function based on a number of parameters that scales linearly with the number of cells. We present a study case with the Mobility Load Balancing (tuning the Cell Individual Offset (CIO)) and Mobility Robustness Optimization (tuning the CIO and the handover hysteresis) functions, where the SONCO deals with the conflicts on the CIOs. Numerical results prove that we can orchestrate the SON functions through SONCO configurations that reflect different operator policies.