  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.
