Hierarchical Genetic Algorithm for dynamic time slot allocation in TD-CDMA TDD systems

Hierarchical Genetic Algorithms (HGA) as a tool for a search and optimizing methodology has now reached a mature stage. 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 channel-borrowing scheme in wireless cellular networks. Therefore, we propose a new efficient HGA Channel Borrowing (HGACB) in distributed cellular networks. The proposed scheme exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding a better performance than other algorithms. 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 delays than previous methods.

► The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel-acquisition delays than previous methods.