Performance comparison among neural decision feedback equalizers

被引：3

作者：

Di Claudio, ED ^{[1
]}

Parisi, R ^{[1
]}

Orlandi, G ^{[1
]}

机构：

[1] Univ Roma La Sapienza, INFOCOM Dept, I-00184 Rome, Italy

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL V | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.861496

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Neural networks add flexibility to the design of equalizers for digital communications. In this work novel decision-feedback (DF) neural equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of a cost functional based on the Discriminative Learning (DL), coupled with a fast training paradigm, can provide neural equalizers that outperform the standard DF equalizer (DFE) at practical signal to noise ratio (SNR). Resulting architectures are competitive with the Viterbi solution from cost-performance aspects.

引用

页码：361 / 365

页数：5

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