基于神经网络和遗传算法的高密集WLAN 公平性保障算法

In order to meet the growing requirements of users for wireless services in various scenarios, highly densely deployed wireless local area network(WLAN) will be developed. However, due to the limited frequency resources, there must be a large number of wireless access points(APs) in the same channel. But APs which are located in the same channel will interfere with each other, resulting in a decline in the fairness of throughput in the network. And it can not provide users with good quality of service. In order to improve the fairness of throughput and experience of users, it is necessary to formulate the reasonable network parameter adjusting methods. A method based on neural network and genetic algorithm is proposed. The neural network is used to build the relationship between the parameters of WLAN and the fairness of throughput. The trained model is used as the fitness evaluation function of the genetic algorithm. And the genetic algorithm is used to solve the optimization parameter combination configuration to improve the fairness of throughput in WLAN. Simulation results show that the present algorithm can improve the fairness of throughput in the whole network.