基于神经网络和粒子群算法的MC-CDMA多用户检测

MC-CDMA系统是干扰受限系统,存在多址干扰。本文提出一种基于神经网络和粒子群算法的MC-CDMA多用户检测方法。在粒子位置更新中,随机选择部分粒子作为Hopfield神经网络的神经元组成的个体,进行神经网络的更新;其他粒子仍采用原粒子群算法的位置更新策略,从而能加快粒子群算法的收敛速度以及降低计算复杂度。仿真表明在算法参数相同时,该多用户检测方法在误码率、收敛速度、系统容量、抗远近能力等方面都优于基于粒子群算法的多用户检测和基于神经网络的多用户检测,更加逼近于最佳多用户检测的性能。

A Multiuser Detection for MC-CDMA System Based on Particle Swarm Optimization Algorithm with Hopfield Neural Network

There is multiple access interference(MAI)in MC-CDMA which is a interference limited system.This paper presents a multiuser detection(MUD)for MC-CDMA system based on particle swarm optimization(PSO)algorithm with Hopfield neural network(HNN).In the updating of particles position,choosing some random particles as individuals composed of neurons in HNN to update the network,and employing PSO updating strategy to the others,which can provide faster rate of convergence and reduce the computational complexity of...