A Novel Handover Self-Optimization Algorithm for 4G and Beyond Networks

Abstract

The recently emerging fourth generation networks, notably LTE-Advanced, are expected to meet the requirements of higher bit rates with excellent quality of service. The expansion and the heterogeneity of these networks have made their operational cost higher. Therefore, automatic engineering has been recently addressed as a feature for remote network managing while minimizing human intervention. Our paper is part of this context. We propose a novel framework based on Statistical Learning, Fuzzy Logic and Reinforcement Learning for Handover parameter self-tuning followed by Handover self-optimization based on Load Balancing in LTE-Advanced networks. We aim at optimizing some Key Performance Indicators (KPIs) such as cells load, Call Drop Rate and Call Blocking Rate.