Federated Deep Q-Learning and 5G load balancing

Hsin Lin, Yi-Kang Su, Hong-Qi Chen, La-Fei Ko
2/10/2024
cs.NIcs.AIcs.LG

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Abstract

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

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Cite this paper

@article{lin2024federated,
  title  = {Federated Deep Q-Learning and 5G load balancing},
  author = {Hsin Lin and Yi-Kang Su and Hong-Qi Chen and La-Fei Ko},
  year   = {2024},
  eprint = {2403.08813},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.08813v1}
}

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