Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Contract Theory Based on Wireless Energy Harvesting with Transmission Performance Optimization

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  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_40,
        author={Chen Liu and Hong Peng and Weidang Lu and Zhijiang Xu and Jingyu Hua},
        title={Contract Theory Based on Wireless Energy Harvesting with Transmission Performance Optimization},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Contract theory Wireless Energy Harvesting Optimal algorithm Performance optimization},
        doi={10.1007/978-3-030-00557-3_40}
    }
    
  • Chen Liu
    Hong Peng
    Weidang Lu
    Zhijiang Xu
    Jingyu Hua
    Year: 2018
    Contract Theory Based on Wireless Energy Harvesting with Transmission Performance Optimization
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_40
Chen Liu1,*, Hong Peng1,*, Weidang Lu1,*, Zhijiang Xu1,*, Jingyu Hua1,*
  • 1: Zhejiang University of Technology
*Contact email: 1078017312@qq.com, ph@zjut.edu.cn, luweid@zjut.edu.cn, zyfxzj@zjut.edu.cn, eehjy@zjut.edu.cn

Abstract

In this paper, we proposed a contract theory on optimization of wireless energy collection and transmission systems. Its purpose is to maximize the transmission rate of the source node to the destination node. Source node broadcasts signal to relay node. We assume that the quality of the link between the source node and the destination node link is poor, and the signal cannot be directly transmitted to the destination node. Relay node have no energy to forward the signal. At this time, the relay node needs energy from surrounding energy access points (EAPs) and the destination node will pay corresponding rewards. We designed the optimal contract theory in order to maximize the transmission performance of the source node. Finally, we use the optimal algorithm to get the best result.