Federated Deep Q-Learning and 5G load balancing
This addresses load balancing for cellular networks, but it is incremental as it applies an existing federated learning approach to a known problem.
The paper tackles base station load balancing in 5G networks by using federated deep Q-learning to enable user equipment to independently select base stations based on load conditions, resulting in consistently better average quality of service compared to the maximum Signal-to-Noise-Ratio method.
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