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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reinforcement Learning Based Mobility Load Balancing with the Cell Individual Offset


    Contributors:


    Publication date :

    2021-04-01


    Size :

    1767812 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Reinforcement Learning Based Load Balancing Routing for LEO Satellite Network

    Zuo, Peiliang / Wang, Chen / Wei, Zhanzhen et al. | IEEE | 2022


    Load Balancing for Mobility-on-Demand Systems

    Pavone, Marco / Smith, Stephen L. / Frazzoli, Emilio et al. | NTRS | 2011


    Load balancing for mobility-on-demand systems

    Rus, Daniela / Frazzol, Emilio / Smith, Stephen L. et al. | NTRS | 2011



    Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations

    Madelkhanova, Aida / Becvar, Zdenek / Spyropoulos, Thrasyvoulos | IEEE | 2022