Research On WMN Channel Assignment Based On Genetic Game Optimization Algorithm

With the innovative technology, wireless networks is more and more widel y applied in people’s life. But the shortage resource result from the explosion of population and a waste of useful network resources has become one of the major problems to be solved today. Since Wireless Mesh Networks borned, it gr adually replaces the traditional wireless networks and intergrates into and improv es people’s life. However, emerging technologies will also face the problem of r esource allocation. In terms of channel allocation, it should be considered these problem such as throughout, transmission delay, the situation of losing packets a nd fairness of allocation. As for the problem of channel distribution in wireless Mesh networks, many experts and scholars have made discussions to improve i t. But in the aspect of fairness, the existed algorithm cannot adapt to the dyna mic changes of resource. In this paper, the game theory is integrated into the c hannel allocation in wireless mesh networks. The competitive nodes are viewed as participants of the game. They take strategies to seize channel resources. Diff erent from traditional games, the improved algorithm imposed in this paper intro duces the loss factor based on the presence of interference in the channel to dy namically update the weight of game, leading to achieving the purpose of on-de mand request, and ensuring the fairness of channel allocation, then making use of the genetic algorithm to solve the problem of the Nash equilibrium’s converg ence rate. Because of the slowly convergence rate and prematureness, we propos es an adaptive efficient genetic algorithm to improve the traditional algorithm o n genetic manipulation. In the improved algorithm, we use adaptive crossover pr obability and adaptive mutation probability. The simulation show the new algorit hm is effective. It accelerates the convergence rate of Nash equilibrium.