G. Lee, W. Saad, M. Bennis, A. Mehbodniya and F. Adachi, "Online Ski Rental for ON/OFF Scheduling of Energy Harvesting Base Stations," in IEEE Transactions on Wireless Communications, vol. 16, no. 5, pp. 2976-2990, May 2017. doi: 10.1109/TWC.2017.2672964
Online ski rental for ON/OFF scheduling of energy harvesting base stations
|Author:||Lee, Gilsoo1; Saad, Walid1; Bennis, Mehdi2;|
1Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
2Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
3Department of Communication Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8577, Japan
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018080633433
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2018-08-06
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 consisting of energy consumption and transmission delay of a network. For the original problem, we show that an algorithm can solve the problem in the illustrative case. Then, 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 and a randomized online algorithm (ROA) are developed. The ROA is then shown to achieve the optimal competitive ratio in the approximation problem. Simulation results show that, compared with 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 also 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 with a baseline approach.
IEEE transactions on wireless communications
|Pages:||2976 - 2990|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the U.S. National Science Foundation under Grant CNS-1460333, in part by the Towards Energy-Efficient Hyper-Dense Wireless Networks with Trillions of Devices, the Commissioned Research of National Institute of Information and Communications Technology, Japan, and in part by the Academy of Finland CARMA project.
|Academy of Finland Grant Number:||
289611 (Academy of Finland Funding decision)
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