摘要： 提出基于协进化理论的认知无线电参数跨层优化体系结构和基于协进化粒子群优化算法的认知决策引擎。通过协进化技术将高维粒子降低为低维粒子，提高算法收敛速度和收敛效率。对多载波系统进行仿真分析，结果表明，基于协进化粒子群优化算法的认知决策引擎在收敛速度和运行效率上优于基于二进制粒子群优化和量子遗传的认知决策引擎。

关键词: 认知无线电, 决策引擎, 粒子群优化

Abstract: A Cognitive Radio(CR) cross-layer optimization architecture based on co-evolutionary theory and cognitive decision engine based on co-evolutionary Particle Swarm Optimization(PSO) algorithm are proposed in this paper. For improving the convergence rate and efficiency, the proposed algorithm reduces the high dimensional particle to low dimensional particle by co-evolutionary theory. Simulation results for multi-carrier system show that the proposed cognitive decision engine in convergence rate and operational efficiency are better than the cognitive decision engine based on basic particle swarm optimization and based on quantum genetic.

Key words: Cognitive Radio(CR), decision engine, Particle Swarm Optimization(PSO)

中图分类号: 

• TP18