Self-Adaptive Scheduling of Base Transceiver Stations in Green 5G Networks

In this paper, we design self-adaptive scheduling (SAS) algorithms for base transceiver stations (BTSs) of 5G networks to improve energy efficiency, reduce carbon footprint, and develop a self-sustainable green cellular network. In the SAS algorithm, a BTS switches among its operating states (active, turned-off, and sleep), thereby exploiting the traffic loads of the BTS and the single-hop neighbor BTSs thereof. The dynamic settings of traffic thresholds help the SAS system in achieving a high degree of cooperation among the neighborhood BTSs, which in turn increases the energy savings of the network. Each active SAS BTS independently and dynamically decides in determining its operation state, thus make our proposed SAS algorithms fully distributed. Results from a simulation conducted in network simulator version 3 show that BTS scheduling significantly influences cellular networks, and the proposed SAS algorithm can significantly increase the energy savings compared with state-of-the-art protocols.