认知网络中基于纳什议价解的功率控制方法

Power Control Method Based on Nash Bargaining Solution for Cognitive Radio Networks

基于纳什议价合作博弈论,研究了认知无线电网络(CRN)中的功率控制问题.设计了一种基于信干扰比(SINR)的效用函数,并提出一种基于纳什议价解(NBS)的分布式功率控制算法,可保证系统的帕雷托最优性和认知用户之间的公平性.采用拉格朗日松弛技术获得了各个认知用户的最优传输功率策略,并结合定点迭代技术,大大降低功率控制问题的复杂度.仿真结果表明,所提出算法实现简单且收敛速度较快、鲁棒性较好,与非合作算法相比可以有效改善认知用户之间的公平性和系统的整体性能.

A power control method for cognitive radio networks （CRN） is investigated in the context of cooperative game theory based on the Nash bargaining solution （NBS）. A utility function based on signal-to-interference plus noise （SINR） is proposed for this model. Based on the NBS, the power control method is not only consistent with the fairness axioms of game theory, but also provides the power level of users that are Pareto optimal from the point of view of whole system. Introducing the Lagrange operators, the optimal power levels are achieved, and the SINR threshold requirements are satisfied. Simulations show that our algorithm has a faster convergence speed, and compared to reference traditional method, our algorithm can effectively improve the fairness among multiple cognitive users and the overall performance of the whole cognitive system.