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

Research Article

Dynamic Power Control for Throughput Maximization in Hybrid Energy Harvesting Node

  • @INPROCEEDINGS{10.1007/978-3-319-66628-0_3,
        author={Didi Liu and Jiming Lin and Junyi Wang and Hongbing Qiu and Yibin Chen},
        title={Dynamic Power Control for Throughput Maximization in Hybrid Energy Harvesting Node},
        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 Throughput maximization Hybrid energy sources Lyapunov optimization Wireless communication},
        doi={10.1007/978-3-319-66628-0_3}
    }
    
  • Didi Liu
    Jiming Lin
    Junyi Wang
    Hongbing Qiu
    Yibin Chen
    Year: 2017
    Dynamic Power Control for Throughput Maximization in Hybrid Energy Harvesting Node
    CHINACOM
    Springer
    DOI: 10.1007/978-3-319-66628-0_3
Didi Liu,*, Jiming Lin1, Junyi Wang1, Hongbing Qiu1, Yibin Chen1
  • 1: Guilin University of Electronic Technology
*Contact email: ldd866@gxnu.edu.cn

Abstract

In this paper, we consider a wireless communication node with hybrid energy harvesting (EH) sources which results in great difficulty in obtaining the statistical knowledge of joint EH process. In addition, the wireless channel fluctuates randomly due to fading. Our goal is, under this condition, to develop a dynamic power control policy for the transmitter such that the time average throughput of the system is maximized over an infinite horizon, taking into account the circuit energy consumption and inefficiency of the rechargeable battery. Such a dynamic power control problem is formulated as a stochastic network optimization problem. The problem is solved by utilizing Lyapunov optimization and an efficient on-line algorithm with quite low complexity is obtained. Simulation results illustrate that the proposed algorithm has the same performance as the optimal one with giving statistical knowledge of the stochastic processes.