UE-Assisted Energy Management for Predictive Traffic-Based Cell Switching in Base Stations

In 5G cellular networks, base stations (BS) are the main contributors to energy costs, making their efficiency crucial, especially as we move toward 6G. This paper proposed a scheme that extends the radio resource management measurement for user equipment (UE) handover to support BS energy saving. UEs periodically measure the received signal reference power of the serving and neighboring BSs, reporting both the serving BS and the best BS with the maximum RSRP to the access and mobility management function (AMF). Additionally, BSs report their current and predictive cell traffic loads to the AMF. This information helps the AMF identify BSs that can enter energy-saving (ES) state or return to ACTIVE state, ensuring a balance between energy savings and quality of service. Such a distributed traffic prediction process reduces the AMF’s workload. The scheme also addresses potential handover issues to maximize BSs in ES state. Simulation results demonstrate that the proposed scheme achieves significant network energy savings without sacrificing the quality of services of UEs.