Federated Deep Q-Learning and 5G load balancing
<|reference_start|>Federated Deep Q-Learning and 5G load balancing: Despite advances in cellular network technology, base station (BS) load balancing remains a persistent problem. Although centralized resource allocation methods can address the load balancing problem, it still remains an NP-hard problem. In this research, we study how federated deep Q learning can be used to inform each user equipment (UE) of the each BS's load conditions. Federated deep Q learning's load balancing enables intelligent UEs to independently select the best BS while also limiting the amount of private information exposed to the network.   In this study, we propose and analyze a federated deep Q learning load balancing system, which is implemented using the Open-RAN xAPP framework and the near-Real Time Radio Interface Controller (near-RT RIC). Our simulation results indicate that compared to the maximum Signal-To-Noise-Ratio (MAX-SINR) method currently used by UEs, our proposed deep Q learning model can consistently provide better High average UE quality of service<|reference_end|>
@article{lin2024federated,
    title={Federated Deep Q-Learning and 5G load balancing},
    author={Hsin Lin, Yi-Kang Su, Hong-Qi Chen, La-Fei Ko},
    journal={arXiv preprint arXiv:2403.08813},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08813},
    primaryClass={cs.NI cs.AI cs.LG cs.MA}
}