Back to Search
Start Over
Online ski rental for ON/OFF scheduling of energy harvesting base stations
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers, 2017.
-
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 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.
- Subjects :
- FOS: Computer and information sciences
energy harvesting
Mathematical optimization
Schedule
Transmission delay
online algorithms
Computer science
cellular networks
Computer Science - Information Theory
Network delay
050801 communication & media studies
small cell networks
02 engineering and technology
Energy storage
Scheduling (computing)
ski rental problem
0508 media and communications
0202 electrical engineering, electronic engineering, information engineering
Macrocell
Electrical and Electronic Engineering
Online algorithm
Ski rental problem
Competitive analysis
Information Theory (cs.IT)
Applied Mathematics
05 social sciences
Approximation algorithm
020206 networking & telecommunications
Energy consumption
Computer Science Applications
Cellular network
Energy source
Energy harvesting
optimization
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....2e55b0fb1fae84c985265dad7a2fecb5