基于PSO模糊神经元的异构无线网络接入选择

A PSO-Fuzzy Neuron Based Access Selection in Heterogeneous Wireless Networks

采用模糊逻辑和神经网络技术进行异构无线网络接入选择的方法未合理考虑网络负载状况,为此提出一种对网络负载具有很好动态适应性的基于粒子群优化(PSO)模糊神经元的接入选择方法.该方法将可接入网络的接入阻塞率相等作为模糊神经元参数学习的目标,并结合具有全局寻优能力的PSO算法设定参数初值,提高了参数学习精度.仿真结果表明,该方法能有效实现网络间负载均衡,相对于最大负载均衡算法可降低网络的接入阻塞率.

Aiming at solving the problem that access selection method based on fuzzy logic and neural network technology for heterogeneous wireless network did not consider network load conditions reasonably, a particle swarm optimization （PSO）-fuzzy neuron based access selection algorithm with dynamic adaptability for network load is proposed. This method set equal access blocking rate as a goal for fuzzy neuron parameter learning, and combined with PSO algorithm with global optimization capability to set initial parameters value, so as to improve the precision of parameter learning. Simulations show that the proposed algorithm can balance the load among networks effectively, and reduce the access blocking rate compared with maximum load balance algorithm.