用于能量收集基站的ON/OFF调度的在线滑雪板租赁
Online Ski Rental for ON/OFF Scheduling of Energy Harvesting Base Stations
摘要：
小小区基站（SBS）与传统宏小区基站的共存是提高蜂窝网络容量和覆盖的一种有前途的方法。然而，通过病毒式部署SBS来增加网络密度会显著增加能耗。为了减少对不可持续能源的依赖，可以采用完全依赖能量收集的自供电SBS。由于能量到达的不确定性和能量存储系统的有限容量，自供电SBS必须智能地优化其ON和OFF调度。本文研究了在能量收集不确定性条件下，以最小化网络的能量消耗和传输时延组成的运营成本为目标的自供电基站的开/关调度问题.对于原问题，我们证明了一个算法可以解决这个问题的说明性的情况下。为了降低原问题的复杂性，提出了一个近似。为了解决近似问题，滑雪租赁框架，一个强大的在线优化工具，提出了一种新的方法。使用这种方法，每个SBS可以有效地自主决定其ON/OFF时间表，而无需任何关于未来能量到达的先验信息。通过竞争分析，提出了确定性在线算法（DOA）和随机化在线算法（ROA）。在近似问题中，ROA可以达到最优的竞争比。仿真结果表明，与基线方法相比，ROA可以产生性能增益达到15.6%，减少SBS的总能耗和高达20.6%，在每SBS网络延迟减少。结果揭示了影响SBS的ON时间的基本方面，同时表明，与基线方法相比，拟议的ROA可以降低高达69.9%的总成本。
英文摘要：
The co-existence of small cell base stations (SBSs) with conventional macrocell base station is a promising approach to boost the capacity and coverage of cellular networks. However, densifying the network with a viral deployment of SBSs can significantly increase energy consumption. To reduce the reliance on unsustainable energy sources, one can adopt self-powered SBSs that rely solely on energy harvesting. Due to the uncertainty of energy arrival and the finite capacity of energy storage systems, self-powered SBSs must smartly optimize their ON and OFF schedule. In this paper, the problem of ON/OFF scheduling of self-powered SBSs is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs consisted of energy consumption and transmission delay of a network. For the original problem, we show an algorithm can solve the problem in the illustrative case. To reduce the complexity of the original problem, an approximation is proposed. To solve the approximated problem, a novel approach based on the ski rental framework, a powerful online optimization tool, is proposed. Using this approach, each SBS can effectively decide on its ON/OFF schedule autonomously, without any prior information on future energy arrivals. By using competitive analysis, a deterministic online algorithm (DOA) and a randomized online algorithm (ROA) are developed. ROA is shown to achieve the optimal competitive ratio in the approximation problem. Simulation results show that, compared to a baseline approach, the ROA can yield performance gains reaching up to 15.6% in terms of reduced total energy consumption of SBSs and up to 20.6% in terms of per-SBS network delay reduction. The results shed light on the fundamental aspects that impact the ON time of SBSs while demonstrating that the proposed ROA can reduce up to 69.9% the total cost compared to a baseline approach.
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