Neural FIR adaptive Laguerre equalizer with a gradient adaptive amplitude for nonlinear channel in communication systems

作     者：ZHAO HaiQuan ZHANG JiaShu 

作者机构：Key Laboratory of Signal and Information Processing of Sichuan Province Southwest Jiaotong University Chengdu 610031 China 

出 版 物：《Science in China(Series F)》 (中国科学（F辑英文版）)

年 卷 期：2009年第52卷第10期

页      面：1881-1890页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 

基　　金：Supported partially by the National Natural Science Foundation of China (Grant No. 60971104) the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794) the Doctoral Innovation Fund of Southwest Jiaotong University 

主　　题：decision feedback nonlinear channel adaptive equalizer neural network Laguerre filter 

摘      要：To mitigate the linear and nonlinear distortions in communication systems, two novel nonlinear adaptive equalizers are proposed on the basis of the neural finite impulse response （FIR） filter, decision feedback architecture and the characteristic of the Laguerre filter. They are neural FIR adaptive decision feedback equalizer （SNNDFE） and neural FIR adaptive Laguerre equalizer （LSNN）. Of these two equalizers, the latter is simple and with characteristics of both infinite impulse response （IIR） and FIR filters; it can use shorter memory length to obtain better performance. As confirmed by theoretical analysis, the novel LSNN equalizer is stable （0 〈α〈1）. Furthermore, simulation results show that the SNNDFE can get better equalized performance than SNN equalizer, while the latter exhibits better performance than others in terms of convergence speed, mean square error （MSE） and bit error rate （BER）. Therefore, it can reduce the input dimension and eliminate linear and nonlinear interference effectively. In addition, it is very suitable for hardware implementation due to its simple structure.