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合理的认知引擎参数设置可以提高频谱的使用性能.通过分析认知无线网络中的认知引擎参数配置,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免疫多目标优化的求解方法.算法使用Logistic混沌映射初始化种群,并在每一代将混沌特性用于最优解集的搜索;设计了适合此问题的免疫克隆算子和抗体群更新算子,保证了Pateto最优解集分布的多样性和均匀性.最后,在多载波环境下对算法进行了仿真实验.结果表明,算法可以根据信道条件和用户服务的动态变化,自适应调整各个子载波的发射功率和调制方式,可以求出更多满足偏好需求的解,满足认知引擎参数优化要求.
Reasonable parameters of cognitive engine can improve the performance of the spectrum.By analyzing the configuration of cognitive engine parameters in cognitive wireless networks, the mathematical model is given and transformed into a multi-objective optimization problem, and then a The method is based on chaotic immune multi-objective optimization algorithm. The algorithm uses Logistic chaotic mapping to initialize the population and uses chaotic properties in each generation for the search of the optimal solution set. The immune clonal operators and antibody group renewal operators , Which guarantees the diversity and uniformity of the Pateto optimal solution set distribution.Finally, the simulation experiment is carried out under multi-carrier environment.The results show that the algorithm can adaptively adjust each sub-channel according to the channel conditions and the dynamic changes of user services Carrier transmit power and modulation can find more solutions to meet the needs of the preferences to meet the cognitive engine parameter optimization requirements.