mirage

Nonlinear channel equalization for wireless communication systems using Legendre neural networks

DSpace/Manakin Repository

 

Search DR-NTU


Advanced Search Subject Search

Browse

My Account

Nonlinear channel equalization for wireless communication systems using Legendre neural networks

Show simple item record

dc.contributor.author Patra, Jagdish Chandra
dc.contributor.author Meher, Pramod Kumar
dc.contributor.author Chakraborty, Goutam
dc.date.accessioned 2011-09-29T08:13:04Z
dc.date.available 2011-09-29T08:13:04Z
dc.date.copyright 2009
dc.date.issued 2011-09-29
dc.identifier.citation Patra, J. C., Meher, P. K., & Chakraborty, G. (2009). Nonlinear channel equalization for wireless communication systems using Legendre neural networks. Signal Processing, 89(11), 2251-2262.
dc.identifier.issn 0165-1684
dc.identifier.uri http://hdl.handle.net/10220/7126
dc.description.abstract In this paper, we present a computationally efficient neural network (NN) for equalization of nonlinear communication channels with 4-QAM signal constellation. The functional link NN (FLANN) for nonlinear channel equalization which we had proposed earlier, offers faster mean square error (MSE) convergence and better bit error rate (BER) performance compared to multilayer perceptron (MLP). Here, we propose a Legendre NN (LeNN) model whose performance is better than the FLANN due to simple polynomial expansion of the input in contrast to the trigonometric expansion in the latter. We have compared the performance of LeNN-, FLANN- and MLP-based equalizers using several performance criteria and shown that the performance of LeNN is superior to that of MLP-based equalizer, in terms of MSE convergence rate, BER and computational complexity, especially, in case of highly nonlinear channels. LeNN-based equalizer has similar performance as FLANN in terms of BER and convergence rate but it provides significant computational advantage over the FLANN since the evaluation of Legendre functions involves less computation compared to trigonometric functions.
dc.format.extent 13 p.
dc.language.iso en
dc.relation.ispartofseries Signal processing
dc.rights © 2009 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Signal Processing, Elsevier.  It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI: http://dx.doi.org/10.1016/j.sigpro.2009.05.004].
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
dc.title Nonlinear channel equalization for wireless communication systems using Legendre neural networks
dc.type Journal Article
dc.contributor.school School of Computer Engineering
dc.identifier.doi http://dx.doi.org/10.1016/j.sigpro.2009.05.004
dc.description.version Accepted version
dc.identifier.rims 142401

Files in this item

Files Size Format View
36. Nonlinear c ... gendre neural networks.pdf 762.5Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Statistics

Total views

All Items Views
Nonlinear channel equalization for wireless communication systems using Legendre neural networks 906

Total downloads

All Bitstreams Views
36. Nonlinear channel equalization for wireless communication systems using Legendre neural networks.pdf 564
2009_SIGPRO3794_Cheq_LeNN.pdf 60

Top country downloads

Country Code Views
Singapore 213
United States of America 140
India 93
China 69
Iraq 12

Top city downloads

city Views
Singapore 212
Mountain View 109
Beijing 55
Baghdad 12
New Delhi 12