Hierarchical Genetic Algorithms for Channel Allocation in Wireless Networks
Hierarchical genetic algorithms (HGA) as a tool for a search and optimizing methodology has now reached a mature stage. The minimum resource facility to carry user traffic which is called a channel unit (CU) is composed of the IEEE 802.16 medium access control (MAC) protocol. The control of the number of CU depending on traffic load solves heterogeneous and asymmetrical traffic problem in 4G system. In a cellular network, the call arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. Whether the criteria of concern be nonlinear, constrained, discrete or NP hard. In this paper, the HGA is used to tackle the neural network (NN) topology as well as the fuzzy logic controller for the dynamic CUs allocation scheme in wireless access net-works. Therefore, we propose a new efficient HGA CUs allocation (HGACA) in cellular networks. It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements of multimedia traffic. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel acquisition delay than previous methods.