  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.
