Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks
Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks
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DOI：
10.1109/tgcn.2019.2931700
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发表时间：
2019-12-01
影响因子：
4.8
通讯作者：
Schulzrinne, Henning
中科院分区：
文献类型：
--
作者：
Celebi, Haluk;Yapici, Yavuz;Schulzrinne, Henning
小型小区的密集部署被视为解决下一代5G无线网络流量需求的主要方法之一。然而，随着大量小小区的部署，能量效率沿着成为关注点。在本文中，我们认为能源有效的小小区网络（SCN）使用智能开/关调度（OOS）策略，其中一定比例的小基站（SBS）被置于低能耗的睡眠状态，以节省能源。为此，我们首先表示整体SCN流量的一个新的负载变量，并严格使用伽马近似分析其统计数据。然后，我们提出了两个新的OOS算法，利用这个负载变量集中和分布式的方式。我们表明，建议基于负载的OOS算法可以导致高达50%的节能，而不牺牲平均SCN吞吐量。此外，基于负载的策略工作良好，在高SCN流量和延迟不容忍的情况下，可以有效地使用负载统计。我们还表明，基于负载的算法的性能得到最大化的一定长度的睡眠期，其中假设短的睡眠期是保持SBS在睡眠状态很长一段时间的能源效率低。
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 concern along with the deployment of massive amount of small cells. In this paper, we consider the energy-efficient small cell networks (SCNs) using smart on/off scheduling (OOS) strategies, where a certain fraction of small base stations (SBSs) 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.