5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings

Research Article

Multi-base Station Energy Cooperation Based on Nash Q-Learning Algorithm

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  • @INPROCEEDINGS{10.1007/978-3-319-72823-0_7,
        author={Yabo Lv and Baogang Li and Wei Zhao and Dandan Guo and Yuanbin Yao},
        title={Multi-base Station Energy Cooperation Based on Nash Q-Learning Algorithm},
        proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings},
        proceedings_a={5GWN},
        year={2018},
        month={1},
        keywords={Multi-agent reinforcement learning Nash equilibrium Q-learning Energy harvesting},
        doi={10.1007/978-3-319-72823-0_7}
    }
    
  • Yabo Lv
    Baogang Li
    Wei Zhao
    Dandan Guo
    Yuanbin Yao
    Year: 2018
    Multi-base Station Energy Cooperation Based on Nash Q-Learning Algorithm
    5GWN
    Springer
    DOI: 10.1007/978-3-319-72823-0_7
Yabo Lv1,*, Baogang Li1,*, Wei Zhao1,*, Dandan Guo1,*, Yuanbin Yao1,*
  • 1: North China Electric Power University
*Contact email: yabolv@163.com, baogangli@ncepu.edu.cn, andyzhaoster@163.com, guodanstyle@163.com, hdyaoyuanbin@outlook.com

Abstract

In view of the current energy problems of communication base station, a multi-base station energy cooperation strategy is proposed to reduce the energy consumption of power grid, which is introducing renewable energy and energy cooperation between the base station based on the Nash-Q learning algorithm. We analyze the packet rate and throughput of the system under the proposed approach. The simulation results show that the proposed algorithm can enhances the adaptability to the changing environment, effectively improve the system capacity.