摘要： 针对经典离散粒子群优化算法收敛性差的缺点，设计了基于新的运动方程的离散粒子群优化算法。为了解决CDMA系统多用户检测这个NP完全问题，基于免疫克隆选择理论和新的粒子群优化算法，提出了克隆粒子群优化算法，其中，由神经元构成的粒子可以进行随机搜索和经验学习。仿真结果表明，在异步和同步CDMA系统上，该检测器的误码率性能都优于传统方法和其他一些多用户检测器，达到最优检测。

关键词: 多用户检测, 粒子群优化算法, 克隆选择算法, Hopfield神经网络

Abstract: To resolve local optimization of the standard discrete Particle Swarm Optimization(PSO) algorithm, a Novel Particle Swarm Optimization(NPSO) algorithm with new motion equations is presented. Based on clonal selection theory and NPSO, Clonal Particle Swarm Optimization(CPSO) algorithm is proposed to design multiuser detector in Code Division Multiple Access(CDMA) systems. By using the clonal selection operator and the particle neutron, CPSO can carry out the stochastic search and experience learning. Simulation results for synchronous and asynchronous cases are provided to show that CPSO-based detector is superior to the conventional detector and some previous detectors in bit error rate, and has optimal performance like optimum multiuser detector.

Key words: Multiuser Detection(MD), Particle Swarm Optimization(PSO) algorithm, clonal selection algorithm, Hopfield Neural Network(HNN)

中图分类号: 

• TN914