Abstract:In order to treat the problem of parameter reconfiguration of the cognitive radio system

an improved hybrid cross-layer decision engine based on the Cellular Quantum Artificial Bee Colony Algorithm (CQABC) and the channel case base was proposed. In the decision engine

the parameters at different layers of a wireless communication network were considered and the overall performance of the network was the optimization goal. A fast strategy with quantum rotation angle adjustment based on cellular automata and social cognitive strategy and two kinds of chaos initialization methods were used in the proposed CQABC. Furthermore

the historical experience and expertise was referred to build up the case library of the cognitive radio parameters based on the channel gain for a quick decision-making process. The results of simulation showed that the cross-layer decision engine is capable of dynamic re-configuration of parameters according to changes in the wireless communication environment and user requirements

on the meantime the proposed decision engine has better convergence

precision and stability than the traditional decision engine based on Binary artificial bee colony algorithm and quantum genetic algorithm.