基于混沌二进制粒子群算法的认知无线电频谱分配策略

为了解决认知无线电网络中以最大化网络效益为准则的频谱分配难问题，提出一种基于混沌二进制粒子群算法的动态时变频谱分配策略。在该策略中，针对二进制粒子群算法收敛速度慢且后期粒子搜索具有单一性的缺陷，引入混沌映射对初始种群和每代粒子位置进行遍历优化，以提高粒子的全局寻优性能，搭建降维频谱分配数学模型，降低算法计算繁杂度，减少时间开销。实验结果证明，所提算法收敛速率快，可获得较高的网络收益。

Spectrum allocation strategy of cognitive radio based on chaos logistic binary particle swarm optimization algorithm

In order to solve spectrum allocation with maximum network benefit as the criterion in cognitive radio networks, a dynamic time varying spectrum allocation strategy based on chaotic logistic binary particle swarm optimization algorithm is proposed. In this strategy, given that the binary particle swarm optimization has a slow convergence rate and a single particle defect in late search process, the chaotic map is introduced to optimize the initial population and the optimal location of every generation particle. It improves the global optimization performance of particles. A mathematical model is built for dimension reduction spectrum allocation. It reduces the computational complexity of the algorithm and the time cost. The experimental results show that the algorithm has a fast convergence rate and can obtain higher network revenue.