Neural network-based decision feedback equalizer using a recursive least squares algorithm

被引：0
|
作者：
Mahmood, K [1 ]
Zerguine, A [1 ]
机构：
[1] King Fahd Univ Petr & Minerals, Dept Elect Engn, Dhahran 31261, Saudi Arabia
来源：
ISSPA 2005: The 8th International Symposium on Signal Processing and its Applications, Vols 1 and 2, Proceedings | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work a recent v derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used for a decision feedback equalization (DFE) scenario. Its performance is investigated and compared to those of MLP-DFE based on the back propagation (BP) algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improved performance obtained by the new structure in both time-invariant and time-varying fading channels.
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页码：82 / 85
页数：4
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