Linear UCB for Online SON Management

Policy Based SON Management (PBSM) is the process of orchestrating the deployed Self-Organized Network (SON) functions, so that the network responds as a whole to the operator objectives. This process is based on the configuration of the SON functions in order to steer their actions in the network towards certain operator objectives. The PBSM ensures an automated translation of these objectives into configurations of the SON functions. An approach has been recently proposed to empower the PBSM with cognitive capabilities (C-PBSM), using a Multi-Armed Bandit algorithm, namely the UCB1. The C-PBSM learns the optimal SON configurations based on network feedback. In this paper we propose an alternative approach, based on the LinUCB algorithm, that is able to learn the optimal SON configuration much faster than the previous approach. The speed of convergence is a critical factor that has to be thoroughly considered in the deployment of online learning processes. Results are shown using an LTE-A simulator that considers real-like network topology and parameters, and accurate ray tracing based propagation.