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Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

Author

Listed:
  • Jing Zhang
  • Li Lei
  • Xin Feng

Abstract

A group of collaborative nodes can efficiently complete spatial long-distance transmission tasks using beamforming technology. However, a high sidelobe level interferes with communication quality, and uneven energy consumption of nodes affects network lifetime. This paper proposes an energy-efficient collaborative transmission algorithm based on potential game theory for beamforming. First, the minimum number of cooperative nodes is determined in accordance with the energy consumption and spacing limitation condition. A group of nodes satisfying the node spacing condition is selected as cooperative nodes based on the ring array to minimize communication interference among nodes. Second, a potential game model is proposed as a joint method for optimizing the collaborative parameters of the cooperative nodes and their energy consumption balancing features. Finally, the game process is continuously executed until the Nash equilibrium is reached. According to simulation results, the sidelobe level caused by the cooperative nodes is reduced and the transmission conflicts are lessened. Thus, the quality of communication links in between nodes in the network is improved. Energy efficiency is also promoted because a balancing of energy consumption is involved in the proposed potential game model, and network lifetime is effectively prolonged accordingly.

Suggested Citation

  • Jing Zhang & Li Lei & Xin Feng, 2019. "Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
  • Handle: RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719877630
    DOI: 10.1177/1550147719877630
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    Cited by:

    1. Xiaoxiao Yang & Jing Zhang & Jun Peng & Lihong Lei, 2021. "Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing," International Journal of Distributed Sensor Networks, , vol. 17(6), pages 15501477211, June.

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