Load balancing and handover joint optimization in LTE networks using Fuzzy Logic and Reinforcement Learning

With the growing deployment of cellular networks, operators have to devote significant manual effort to network management. As a result, Self-Organizing Networks (SONs) have become increasingly important in order to raise the level of automated operation in cellular technologies. In this context, Load Balancing (LB) and Handover Optimization (HOO) have been identified by industry as key self-organizing mechanisms for the Radio Access Networks (RANs). However, most efforts have been focused on developing a stand-alone entity for each self-organizing mechanism, which will run in parallel with other entities, as well as designing coordination mechanisms in charge of stabilizing the network as a whole. Due to the importance of LB and HOO, in this paper, a unified self-management mechanism based on Fuzzy Logic and Reinforcement Learning is proposed. In particular, the proposed algorithm modifies handover parameters to optimize the main Key Performance Indicators related to LB and HOO. Results show that the proposed scheme effectively provides better performance than independent entities running simultaneously in the network.