摘要： 为解决认知无线电频谱分配的离散优化问题并提高分配性能,提出一种离散量子粒子群优化算法。利用量子计算理论更新粒子并用波函数对量子旋转角进行调节,使之同时具有粒子群优化算法快速收敛和量子计算精度高的优点,从而有效提高认知无线电频谱分配的性能。仿真结果表明,与遗传算法、量子遗传算法、粒子群优化算法和敏感图论着色算法相比,该算法能较快地搜寻到最优解,且在不同的网络效益函数下性能较优。

关键词: 认知无线电, 频谱分配, 离散优化, 离散量子粒子群优化算法, 网络效益函数

Abstract: In order to solve discrete optimization problem in Cognitive Rradio(CR) spectrum allocation and improve the allocation performance,this paper proposes a Discrete Quantum-behaved Particle Swarm Optimization(DQPSO) algorithm.It updates the particles with quantum computing theory and adjusts the rotation angle with wave function,and it has both advantages of fast convergence rate of Particle Swarm Optimization(PSO) algorithm and high precision of quantum computing.When applied in the problem of CR spectrum allocation,it can improve the performance of spectrum allocation effectively.This paper compares the performance of the proposed algorithm with the Genetic Algorithm(GA),Quantum-inspired Genetic Algorithm(QIGA),PSO algorithm and Color Sensitive Graph Coloring(CSGC) algorithm in different network benefit functions.Simulation results show that the proposed algorithm can search faster for the optimal solution,and it has better performance.

Key words: Cognitive Radio(CR), spectrum allocation, discrete optimization, Discrete Quantum-behaved Particle Swarm Optimization(DQPSO) algorithm, network benefit function

中图分类号: 

• TP393