Federated Deep Q-Learning and 5G load balancing
Hsin Lin Yi-Kang Su Hong-Qi Chen La-Fei Ko
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