Communications and Networking. 11th EAI international Conference, ChinaCom 2016 Chongqing, China, September 24-26, 2016, Proceedings, Part II

Research Article

Energy-Efficient Resource Allocation in Energy Harvesting Communication Systems: A Heuristic Algorithm

  • @INPROCEEDINGS{10.1007/978-3-319-66628-0_1,
        author={Yisheng Zhao and Zhonghui Chen and Yiwen Xu and Hongan Wei},
        title={Energy-Efficient Resource Allocation in Energy Harvesting Communication Systems: A Heuristic Algorithm},
        proceedings={Communications and Networking. 11th EAI international Conference, ChinaCom 2016 Chongqing, China, September 24-26, 2016, Proceedings, Part II},
        proceedings_a={CHINACOM},
        year={2017},
        month={10},
        keywords={Energy harvesting communication Resource allocation Heuristic algorithm},
        doi={10.1007/978-3-319-66628-0_1}
    }
    
  • Yisheng Zhao
    Zhonghui Chen
    Yiwen Xu
    Hongan Wei
    Year: 2017
    Energy-Efficient Resource Allocation in Energy Harvesting Communication Systems: A Heuristic Algorithm
    CHINACOM
    Springer
    DOI: 10.1007/978-3-319-66628-0_1
Yisheng Zhao1,*, Zhonghui Chen1,*, Yiwen Xu1,*, Hongan Wei1,*
  • 1: Fuzhou University
*Contact email: zhaoys@fzu.edu.cn, czh@fzu.edu.cn, xu_yiwen@fzu.edu.cn, weihongan@fzu.edu.cn

Abstract

Harvesting energy from the environment is a method to improve the energy utilization efficiency. However, most renewable energy has a poor stability due to the weather and the climate. The reliability of the communication systems will be influenced to a large extent. In this paper, an energy-efficient downlink resource allocation problem is investigated in the energy harvesting communication systems by exploiting wireless power transfer technology. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem. The objective is to maximize the energy efficiency while satisfying the energy causality and the data rate requirement of each user. In order to reduce the computational complexity, a suboptimal solution to the optimization problem is obtained by employing a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the QPSO algorithm has a higher energy efficiency than the traditional particle swarm optimization (PSO) algorithm.