基于克隆遗传量子算法的多用户检测

Multiuser detection based on a clonal genetic quantum algorithm

基于免疫克隆选择理论和遗传量子算法,提出了一种解决CDMA系统多用户检测问题的克隆遗传量子算法.通过使用克隆选择算子和遗传量子算法的理论,新算法能执行随机搜索和经验学习.所提的算法把随机神经网络嵌入到克隆遗传量子算法的每一代中.通过结合随机神经网络到CGQA中,可以加快CGQA的收敛速度、减少计算复杂度.另外,CGQA所提供的好的初值可以改善SHNN的性能,嵌入的SHNN还提高了CGQA的性能.在讨论了使用新算法设计多用户检测器的性能特点后,在CDMA系统进行了计算机仿真并和一些多用户检测器进行了比较.仿真结果证明了文中所提多用户检测器的抗多址干扰能力和抗远近效应能力都优于一些应用以前算法的多用户检测器.

A clonal genetic quantum algorithm （CGQA） is proposed for multiuser detection in code-division multiple-access systems （CDMA）based on antibody clonal selection theory and the genetic quantum algorithm. The new algorithm can carry out a stochastic search with experiential learning by using clonal selection operators and genetic quantum algorithms. This algorithm embeds the stochastic Hopfield neural network （SHNN） into every generation of the CGQA to further improve the fitness of the population in each generation, thus reducing the computational complexity and speeding up the convergence rate. In addition, improved initial data estimation offered by CGQA improves the performance of the SHNN, and the embedded Hopfield neural network improves the performance of the CGQA. After discussing the main characteristics of the proposed receiver, the clonal genetic quantum algorithm multiuser detector （CGQAMUD）, computer simulations are presented and the results compared with those from other detectors in CDMA systems. Simulation results show that the proposed algorithm for multiuser detection is better than other detection methods in terms of resistance to multiple-access-interference and the near-far effect.