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Mathematical Problems in Engineering
Volume 2012, Article ID 636159, 10 pages
Research Article

Robust Nash Dynamic Game Strategy for User Cooperation Energy Efficiency in Wireless Cellular Networks

1Key Laboratory of Industrial Computer Control Engineering of Hebei, Qinhuangdao, Hebei 066004, China
2School of Engineering Science, Simon Fraser University, 250-13450 102 Avenue, Surrey, BC, Canada V3T 0A3
3College of Information Science and Engineering, Yanshan University, Hebei, Qinhuangdao, China
4The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Hebei, Qinhuangdao 066004, China
5College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received 2 October 2012; Accepted 25 October 2012

Academic Editor: Sheng-yong Chen

Copyright © 2012 Shuhuan Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, there is an emerging trend of addressing “energy efficiency” aspect of wireless communications. It has been shown that cooperating users relay each other's information to improve data rates. The energy is limited in the wireless cellular network, but the mobile users refuse to relay. This paper presents an approach that encourages user cooperation in order to improve the energy efficiency. The game theory is an efficient method to solve such conflicts. We present a cellular framework in which two mobile users, who desire to communicate with a common base station, may cooperate via decode-and-forward relaying. In the case of imperfect information assumption, cooperative Nash dynamic game is used between the two users' cooperation to tackle the decision making problems: whether to cooperate and how to cooperate in wireless networks. The scheme based on “cooperative game theory” can achieve general pareto-optimal performance for cooperative games, and thus, maximize the entire system payoff while maintaining fairness.