MLP/BP-based soft DFEs with bit-interleaved TCM for distorted 16-QAM signal recovery in severe ISI channels

In this work, we base on multi-layered perceptron neural networks with backpropagation algorithm (MLP/BP) to construct soft decision feedback equalizers (DFEs). The proposal is used to recover distorted 16-point quadrature amplitude modulation (16-QAM) signal. For better performance, error control c...

Full description

Saved in:
Bibliographic Details
Published in2010 5th International Microsystems Packaging Assembly and Circuits Technology Conference pp. 1 - 4
Main Authors Terng-Ren Hsu, Terng-Yin Hsu, Chien-Ching Lin, Su-Wei Fang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
Subjects
Artificial neural networks
AWGN
Decision feedback equalizers
Decoding
Distortion
MIMO
Online AccessGet full text

Cover

More Information
Summary:In this work, we base on multi-layered perceptron neural networks with backpropagation algorithm (MLP/BP) to construct soft decision feedback equalizers (DFEs). The proposal is used to recover distorted 16-point quadrature amplitude modulation (16-QAM) signal. For better performance, error control codes (ECC) are applied to enhance the accuracy of the transmitted data. From the simulations, we note that the MLP/BP-based soft DFEs with bit-interleaved TCM can recover severe distorted 16-QAM data as well as suppress intersymbol interference (ISI) and background additive white Gaussian noise (AWGN). As compared with the LMS DFE, the proposed scheme can provide better bit-error-rate (BER) and packet-error-rate (PER) performance.
ISBN:1424497833
9781424497836
ISSN:2150-5934
2150-5942
DOI:10.1109/IMPACT.2010.5699585