Game theoretic utility optimization based power control on cognitive sensor Network

Cellular networks are suffering from expanded demands of restricted spectrums. This leads to the phenomenon of cognitive networks. It has been realized that the socio-economics aspects are essentially required with network design, algorithms, and protocols. In this paper, game theoretic utility optimization based power control will be suggested. This optimization will be made using Genetic Algorithm which is optimal evolutionary technique. This maximization will be considered for the minimum utility of pricing based distributed sensing cognitive users. This network depends on complete information on which, each user selects its power strategy that has a better payoff. The pricing - power problem is modeled using the Supermodular game. It is evaluated according to different power strategies that are announced by each user. It is considered as a metric for the interference level in a network. Convergence is characterized by Supermodular game properties as a Nash equilibrium. Simulation results designate that proposed genetic algorithm ameliorates the total average payoff comparing with other classical iterative and distributed algorithms. Moreover, it enhanced the total average payoff gained under provided network constraints.