FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks

The constant surge in the traffic demand on cellular networks has led to continuous expansion in network capacity in order to accommodate existing and new service demands. This has given rise to ultra-dense base station deployment in 5G and beyond networks which leads to increased energy consumption in the network. Hence, these ultra-dense base station deployments must be operated in a way that the energy consumption of the network can be adapted to the spatio-temporal traffic demands on the network in order to minimize the overall energy consumption of the network. To achieve this goal, we leverage two artificial intelligence algorithms, federated learning and actor-critic algorithm, to develop a proactive and intelligent base station switching framework that can learn the operating policy of the small base station in an ultra-dense heterogeneous network (UDHN) that would result in maximum energy saving in the network while respecting the quality of service (QoS) constraints. The performance evaluation reveals that the proposed framework can achieve an energy saving that is about 77% more than that of the state-of-the-art solutions while respecting the QoS constraints of the network.