Reinforcement Learning Based Mobility Load Balancing with the Cell Individual Offset

In this study, we focus on the cell individual offset (CIO) parameter in the handover process, which represents the willingness of a cell to admit the incoming handovers. However, it is challenging to tune the CIO parameter, as any poor implementation can lead to undesired outcomes, such as making the neighboring cells over-loaded while decreasing the traffic load of the cell. In this work, a reinforcement learning-based approach for parameter selection is introduced, since it is quite convenient for dynamically changing environments. In that regard, two different techniques, namely Q-learning and SARSA, are proposed, as they are known for their multi-objective optimization capabilities. Moreover, fixed CIO values are used as a benchmark for the proposed methods for comparison purposes. Results reveal that the reinforcement learning assisted mobility load balancing (MLB) approach can alleviate the burden on the overloaded cells while keeping the neighboring cells at some reasonable load levels. The proposed methods outperform the fixed-parameter solution in terms of the given metric.