Nash Bargaining Game-Theoretic Framework for Power Control in Distributed Multiple-Radar Architecture Underlying Wireless Communication System

Shi, Chenguang; Wang, Fei; Salous, Sana; Zhou, Jianjiang; Hu, Zhentao

Abstract

This paper presents a novel Nash bargaining solution (NBS)-based cooperative game-theoretic framework for power control in a distributed multiple-radar architecture underlying a wireless communication system. Our primary objective is to minimize the total power consumption of the distributed multiple-radar system (DMRS) with the protection of wireless communication user’s transmission, while guaranteeing each radar’s target detection requirement. A unified cooperative game-theoretic framework is proposed for the optimization problem, where interference power constraints (IPCs) are imposed to protect the communication user’s transmission, and a minimum signal-to-interference-plus-noise ratio (SINR) requirement is employed to provide reliable target detection for each radar. The existence, uniqueness and fairness of the NBS to this cooperative game are proven. An iterative Nash bargaining power control algorithm with low computational complexity and fast convergence is developed and is shown to converge to a Pareto-optimal equilibrium for the cooperative game model. Numerical simulations and analyses are further presented to highlight the advantages and testify to the efficiency of our proposed cooperative game algorithm. It is demonstrated that the distributed algorithm is effective for power control and could protect the communication system with limited implementation overhead.

Citation

Shi, C., Wang, F., Salous, S., Zhou, J., & Hu, Z. (2018). Nash Bargaining Game-Theoretic Framework for Power Control in Distributed Multiple-Radar Architecture Underlying Wireless Communication System. Entropy, 20(4), Article 267. https://doi.org/10.3390/e20040267

• Journal Article Type: Article
• Acceptance Date: Apr 6, 2018
• Online Publication Date: Apr 11, 2018
• Publication Date: Apr 11, 2018
• Deposit Date: May 3, 2018
• Publicly Available Date: May 4, 2018
• Journal: Entropy
• Electronic ISSN: 1099-4300
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 4
• Article Number: 267
• DOI: https://doi.org/10.3390/e20040267
• Public URL: https://durham-repository.worktribe.com/output/1331991