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通过分析认知无线网络引擎决策,给出了其数学模型,并将其转化为一个多目标优化问题,进而提出一种基于混沌量子克隆的优化求解算法,并证明了该算法以概率1收敛.算法采用量子编码,利用Logistic映射初始化抗体种群,设计了一种基于混沌扰动的量子变异方案.最后,在多载波环境下对算法进行了仿真实验.结果表明,与QGA-CE(基于量子遗传算法的认知引擎)算法相比,本文算法收敛速度较快,具有较高的目标函数值,可以对无线参数优化调整,满足认知引擎的实时性要求.
By analyzing the decision of cognitive radio network engine, the mathematic model is given and transformed into a multi-objective optimization problem. Then an optimal solution algorithm based on chaotic quantum cloning is proposed and the algorithm converges with probability 1. The algorithm adopts quantum coding and initializes the antibody population by using Logistic map, and designs a quantum mutation scheme based on chaotic perturbation.Finally, the simulation experiment is carried out under multi-carrier environment.The results show that compared with QGA-CE (Quantum Genetic Algorithm Compared with the cognitive engine algorithm, the proposed algorithm converges faster and has a higher objective function value, which can optimize and adjust wireless parameters to meet the real-time requirements of cognitive engine.