[论文解读] Online Ski Rental for ON/OFF Scheduling of Energy Harvesting Base Stations

Gilsoo Lee, Walid Saad+3 more|arXiv (Cornell University)|Feb 1, 2016

Advanced MIMO Systems Optimization参考文献 32被引用 4

一句话总结

本文提出一种基于在线滑雪租赁的算法，用于自主实现能量采集小型基站（SBS）的开关调度，以在能量不确定性条件下最小化运营成本。通过将问题建模为在线优化任务，提出了一种随机在线算法（ROA），与基线方法相比，能量消耗降低最多15.6%，延迟减少20.6%，竞争比为1.86。

ABSTRACT

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.

研究动机与目标

• 为在能量到达不确定且间歇性的情况下，最小化能量采集小型基站（SBS）的运营成本提供解决方案。
• 实现在未来能量可用性未知的情况下，SBS的自主、实时开关调度。
• 在密集蜂窝网络中，降低包括能量消耗和传输延迟在内的总运营成本。
• 开发一种在最坏情况能量到达场景下表现良好的竞争性在线算法。

提出的方法

• 将问题建模为在线滑雪租赁问题，其中SBS必须决定何时从能量采集切换到依赖宏基站（MBS）进行回传。
• 采用竞争分析方法，开发了确定性在线算法（DOA）和随机在线算法（ROA），以确保性能边界。
• 证明ROA在近似问题中可实现最优竞争比1.86，从而最小化最坏情况下的成本。
• 算法根据不断变化的能量成本率和存储能量水平，动态更新关闭时间阈值。
• 引入近似方法以降低计算复杂度，同时保持接近最优的性能。
• 该方法使每个SBS能够仅基于本地能量成本和存储信息，实现去中心化、实时决策。

实验结果

研究问题

• RQ1 在缺乏未来能量到达信息的情况下，SBS如何自主调度其开启和关闭状态？
• RQ2 在不确定性条件下，使用采集能量与购买MBS容量之间的最优权衡是什么？
• RQ3 竞争性在线算法能否在最小化总运营成本方面实现接近最优的性能？
• RQ4 所提出的在线调度算法的理论性能边界（竞争比）是多少？
• RQ5 与基线方法相比，所提出的ROA在能量消耗和延迟降低方面表现如何？

主要发现

• 随机在线算法（ROA）的竞争比为1.86，展现出强大的最坏情况性能保证。
• 与基线方法相比，ROA将总能量消耗最多降低15.6%。
• ROA将每SBS的网络延迟最多降低20.6%。
• 与基线相比，ROA将总运营成本最多降低69.9%。
• DOA和ROA均显著降低了动态能量环境下的切换开销和传输延迟。
• 近似方法在降低计算复杂度的同时，保持了高性能，有利于实时部署。