基于免疫克隆量子算法的多用户检测器  

Multiuser Detector Based on Immune Clonal Quantum Algorithm

作　　者：高洪元[1] 刁鸣[1] 赵忠凯[1] 

机构地区：[1]哈尔滨工程大学信息与通信工程学院,哈尔滨150001

出　　处：《电子与信息学报》2008年第7期1566-1570,共5页Journal of Electronics & Information Technology

基　　金：哈尔滨市科学研究基金(2005AFXXJ033)资助课题

摘　　要：为了解决CDMA系统最佳多用户检测的高计算复杂度问题,基于免疫克隆选择理论和新的遗传量子算法,该文提出了免疫克隆量子算法。该算法把根据神经网络制作的疫苗接种到克隆量子算法的每一代中,通过接种疫苗到CQA中,可以加快CQA的收敛速度减少计算复杂度。另外,CQA所提供的好的初值可以改善疫苗的性能,接种的疫苗还改善了CQA的性能,文中给出了在免疫克隆量子算法中使用随机神经网络制作疫苗的统一理论框架结构。仿真结果证明了该方法不仅能够快速收敛到全局最优解,并且无论抗多址干扰能力和抗远近效应能力都优于传统检测器和一些应用以前智能计算算法的多用户检测器。Based on the immune clonal selection theory and the novel genetic quantum algorithm, an Immune Clonal Quantum Algorithm （ICQA） is proposed to solve high complexity of optimum multiuser detection in code division multiple access systems. Using this algorithm, the vaccine based on Hopfield neural network is inoculated into the Clonal Quantum Algorithm （CQA） to improve further the fitness of the population at each generation. Such a hybridization of the CQA with the stochastic Hopfield neural network reduces its computational complexity by providing faster convergence. In addition, a better initial data estimation supplied by the CQA improves the performance of the vaccine, and the inoculated vaccine improves the performance of the CQA. The uniform theoretic framework of the making vaccine based on the stochastic Hopfield neural network is presented. Simulation results show that the proposed detector not only can achieves the global optimization value in fast convergence rate, but also is obviously superior to the conventional detector and the multiuser detectors based on previous intelligent algorithms in cancellation of the multiple access interference and the near-far effect.

关 键 词：多用户检测 遗传量子算法 HOPFIELD神经网络 克隆选择算法 免疫算法 

分 类 号：TN914[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]