Contract Theory Based on Wireless Energy Harvesting with Transmission Performance Optimization

  • Chen LiuEmail author
  • Hong Peng
  • Weidang Lu
  • Zhijiang Xu
  • Jingyu Hua
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


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.


Contract theory Wireless Energy Harvesting Optimal algorithm Performance optimization 


  1. 1.
    Hou, Z., Chen, H., Li, Y., Vucetic, B.: Incentive mechanism design for wireless energy harvesting-based Internet of Things. IEEE Internet Things J. (2017). Scholar
  2. 2.
    Lu, W., He, C., Lin, Y.: Contract theory based cooperative spectrum sharing with joint power and bandwidth optimization. Project funded by China Postdoctoral Science Foundation under Grand No. 2017M612027Google Scholar
  3. 3.
    Liu, J., Ding, H., Cai, Y., Yue, H., Fang, Y., Chen, S.: An energy-efficient strategy for secondary users in cooperative cognitive radio networks for green communications. IEEE J. Sel. Areas Commun. 34(12), 3195–3207 (2016)CrossRefGoogle Scholar
  4. 4.
    Chen, H., Li, Y., Han, Z., Vucetic, B.: A stackelberg game-based energy trading scheme for power beacon-assisted wireless-powered communication. In: Proceedings of ICASSP, pp. 3177–3181, April 2015Google Scholar
  5. 5.
    Sarma, S., Kandhway, K., Kuri, J.: Robust energy harvesting based on a Stackelberg game. IEEE Wirel. Commun. Lett. 5(3), 336–339 (2016)CrossRefGoogle Scholar
  6. 6.
    Ma, Y., Chen, H., Lin, Z., Li, Y., Vucetic, B.: Distributed and optimal resource allocation for power beacon-assisted wireless-powered communications. IEEE Trans. Commun. 63(10), 3569–3583 (2015)CrossRefGoogle Scholar
  7. 7.
    Li, Y., Wang, W., Kong, J., Peng, M.: Subcarrier pairing for amplify-and-forward and decode-and-forward OFDM relay links. IEEE Commun. Lett. 13(4), 209–211 (2009)CrossRefGoogle Scholar
  8. 8.
    Zhong, C., Suraweera, H., Zheng, G., Krikidis, I., Zhang, Z.: Wireless information and power transfer with full duplex relaying. IEEE Trans. Commun. 62(10), 3447–3461 (2014)CrossRefGoogle Scholar
  9. 9.
    Ju, H., Zhang, R.: Throughput maximization in wireless powered communication networks. IEEE Trans. Wirel. Commun. 13(1), 418–428 (2014)CrossRefGoogle Scholar
  10. 10.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Chen Liu
    • 1
    Email author
  • Hong Peng
    • 1
  • Weidang Lu
    • 1
  • Zhijiang Xu
    • 1
  • Jingyu Hua
    • 1
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouPeople’s Republic of China

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