WSEAS Transactions on Signal Processing


Print ISSN: 1790-5052
E-ISSN: 2224-3488

Volume 13, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


A New Training Strategy for DFE-MLP Using Modified BP Algorithm and Application to Channel Equalization

AUTHORS: Samir Saidani, Abd Elkrim Moussaoui, Mohamed Ghadjati

Download as PDF

ABSTRACT: In this work, a new training strategy using a new modified back-propagation (BP) algorithm for a multilayer perceptron (MLP) based upon the previously introduced in [8], [9] is proposed. Its performance is investigated and compared to those of MLP-DFE based on the standard BP algorithm and the previously introduced in [8], [9]. The results show improved performance obtained by the new structure in nonlinear channels.

KEYWORDS: Multi layer perceptron (MLP), Decision feedback equalizer (DFE), Back-propagation (BP), Channel Equalization, Digital Communication.

REFERENCES:

[1] M. N. Tehrani, M. Shakhsi, H. Khoshbin, Kernel Recursive Least Squares-Type Neuron for Nonlinear Equalization, IEEE 21st Conference on Electrical Engineering, Mashhad, Iran, May 14-16, 2013.

[2] K. Mahmood, A. Zidouri, A. Zerguine, Performance analysis of a RLS-based MLPDFE in time-invariant and time-varying channels, Digital Signal Processing, Vol.18, No.3, 2008, pp. 307-320.

[3] A. Zerguine, A. Shafi, M. Bettayeb, Multilayer perceptron based DFE with lattice structure, IEEE Transaction on Neural Network, Vol.12, No.3, 2008, pp. 532-545.

[4] P. Sivakumar, L. Arthi, Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), Vol.6, No.1, 2013, pp. 34-43.

[5] S. Panda, P.K. Mohapatra, S.P. Panigrahi, A new training scheme for neural networks and application in non-linear channel equalization, Applied Soft Computing, Vol.27, 2015,pp.47- 52.

[6] M. Ibnkahla, Applications of neural networks to digital communication-Asuvey, Signal Processing, Vol.80, No.7, 2000, pp. 1185-1215.

[7] S. Panda, A. Sarangi, S. P. Panigrahi, A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization, AEU - International Journal of Electronics and Communications, Vol.68, No.11, 2014, pp. 1031-1036.

[8] C. M. Lee, S. S. Yang, C.L. Ho, Modified back-propagation algorithm applied to decision-feedback equalisation, Image Signal Process, Vol.153, No.6, 2006, pp. 805-809.

[9] S.S. Yang, C.L. Ho, C.M. Lee, HBP: Improvement in BP Algorithm for an Adaptive MLP Decision Feedback Equalizer, IEEE Transactions On Circuits And Systems, Vol.53, No.3, 2006, pp. 240-244.

[10] S. Qureshi, Adaptive equalization, Proceeding of IEEE, Vol.73, No.9, 1985, pp. 1349-1387.

[11] R. S. Scalero, N. Tepedelenlioglu, A fast new algorithm for training feedforward neural networks, IEEE Transactions on Signal Processing, Vol.40, No.1, 1992, pp. 202-210.

WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 13, 2017, Art. #13, pp. 115-120


Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Bulletin Board

Currently:

The editorial board is accepting papers.


WSEAS Main Site