认知决策引擎的设计是认知无线电系统中的一项关键技术,它的主要功能是依据通信环境的变化和用户需求动态地配置无线电工作参数.提出了一种基于自适应蚁群算法的认知决策引擎来实现工作参数的最优化配置.该算法在基本蚁群算法的基础上加入了路径选择机制和信息素挥发因子自适应调整机制,保证了算法的全局搜索能力和收敛速度,有效地避免了容易陷入局部最优解的缺陷.仿真结果表明,在不同的环境下基于该算法的认知引擎比GA和ACO算法具有更好的性能.%Cognitive decision engine is a key technology in cognitive communication system. Cognitive engine can dynamically configure its working parameters according to the changes of communication environment and users' requirement An adaptive ant colony optimization (AACO) cognitive radio engine was proposed to achieve the optimal configuration working parameters. The novel algorithm based on the basic ant colony algorithm improves the path selection mechanism and adaptively adjusting pheromone decay parameter mechanism. Therefore, it can ensure the global search ability and convergence speed, and effectively avoid falling into local optimization result Simulation results show that the AACO engine has better performance than GA and ACO engines in different scenarios.
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