Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks

Dense deployment of small cells is seen as one of the major approaches for addressing the traffic demands in next-generation 5G wireless networks. The energy efficiency, however, becomes a key concern together with this massive amount of small cells. In this study, we therefore consider the energy-efficient small cell networks (SCN) using smart on/off scheduling (OOS) strategies, where a certain fraction of small base stations (SBS) are put into less energy-consuming sleeping states to save energy. To this end, we first represent the overall SCN traffic by a new load variable, and analyze its statistics rigorously using Gamma approximation. We then propose two novel OOS algorithms exploiting this load variable in centralized and distributed fashions. We show that proposed load based OOS algorithms can lead to as high as 50% of energy savings without sacrificing the average SCN throughput. In addition, load based strategies are shown to work well under high SCN traffic and delay-intolerant circumstances, and can be implemented efficiently using the load statistics. We also show that the performance of load based algorithms gets maximized for certain length of sleeping periods, where assuming short sleep periods is as energy-inefficient as keeping SBSs in sleep states for very long.