基于二进制量子粒子群算法的认知无线电决策引擎

Cognitive radio decision engine based on binary quantum particle swarm optimization

认知无线电决策引擎的设计是认知系统中的一项关键技术,其中一项功能是根据环境改变,利用多目标优化策略,优化传输参数,在不同的通信环境下进行决策。为了适应认知无线电通信系统中环境复杂多变的特点。给出了一种基于二进制具有量子行为的粒子群算法的认知无线电决策方法,因为改进算法是基于量子理论,具备非线性和不确定性的特性,所以在寻优效果上具备明显的优势。在多载波系统上进行仿真,实验证明,相对于传统的认知引擎寻优算法,该算法具有收敛速度快、稳定性好、平均适应度值高的特点,能够很好地满足认知无线电决策引擎中对多目标优化决策的需要。

Cognitive radio decision engine design is a key technology in cognitive communication system.One of its functions is optimizing transmission parameters according to environment change and obtaining desired communication performance using multi-objective optimization algorithm.In this paper,we analyze the cognitive radio decision engine based on OFDM system and introduce a binary quantum-behaved particle swarm optimization algorithm（BQPSO）,which has stronger optimal searching ability and faster convergence speed.Because quantum effect has excellent characteristics of nonlinearity and uncertainty,the algorithm can reach better optimizing performance than other optimization algorithms.Simulation results show that BQPSO algorithm has good performance in convergence,stability,and average fitness value.The algorithm can greatly satisfy the demand of cognitive radio decision engine.