Paper Titles in Periodical
International Letters of Chemistry, Physics and Astronomy
ILCPA Volume 55

Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

ILCPA > ILCPA Volume 55 > Neural Networks Based Equalizer for Signal...
< Back to Volume

Neural Networks Based Equalizer for Signal Restoration in Digital Communication Channels

Full Text PDF


One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and restore the transmitted signal. This paper presents the equalization of digital communication channels using artificial neural network structures. The performances of a nonlinear equalizer using multilayer perceptron (MLP) trained by the back propagation algorithm is compared with a conventional linear traversal equalizer (LTE). Simulation results show that the performances of the MLP based Equalizer surpass significantly the classical LTE in term of the restored signal, the steady state mean square error (MSE) achievable and the minimum bit error rate attainable. The consistency in performance is observed in minimum phase and non-minimum phase channels as well.


International Letters of Chemistry, Physics and Astronomy (Volume 55)
Z. Zerdoumi et al., "Neural Networks Based Equalizer for Signal Restoration in Digital Communication Channels", International Letters of Chemistry, Physics and Astronomy, Vol. 55, pp. 187-200, 2015
Online since:
July 2015

[1] S.U.H. Qureshi, 'Adaptive equalization, ', IEEE Proceeding, vol. 73 no 9, pp.1349-1387, (1985).

[2] P.D. Power, Non-linear Multilayer Perceptron channel equalisation, PHD Dissertation, University of Belfast, (2001).

[3] G.J. Gibson, S. Sui, C. Cowan, Multilayer Perceptron structures applied to adaptive equalisation for data communication', IEEE.

[4] B. Mulgrew, Applying radial basis function networks, IEEE Signal Processing Magazine, p.50–65, Mar. (1996).

[5] I. Santamaria, D. Erdogmus, J.C. Principe, Entropy Minimization for Supervised Digital Communications Channel Equalization, IEEE Transactions on S. Processing, vol. 5, no. 5, pp.1184-1192, May. (2002).


[6] M. Ibnnkahla, Application of neural networks to digital communication, Signal processing 80, pp.1185-1215, (2000).

[7] A. Zerguine, A. Shafi, and M. Bettayeb, Multilayer perceptron based DFE with lattice structure, IEEE Transaction on Neural Networks, vol. 12, no. 3, p.532–545, May. (2001).


[8] J. Feng, C.K. Tse, F.C.M. Lau, ' A Neural network Based Channel Equalisation Strategy for chaos Based Communication Systems, ', IEEE TCS, vol. 50, N0. 7, (2003).

[9] J. Choi, C. Lima and S. Haykin Kalman Filter-Trained Recurrent Neural Equalizers for Time-Varying Channels, IEEE Transaction on communications, vol. 53, n° 3 Mar. (2005).


[10] S. Haykin, Neural Networks: A comprehensive Foundation, New York Macmillan, (1994).

[11] J.C. Patra, P. Meher, and G. Chakraborty. Nonlinear channel equalization for wireless communication systems using Legendre neuralnetworks, Signal Processing 89, PP 2251–2262, (2009).


[12] P.K. Khuntia, B. Sahu and C.S. Mohanty. Development of adaptive channel equalization using DE, World Congress on Information and Communications Technologies, 2012. ( Received 01 June 2015; accepted 30 June 2015 ).

Show More Hide
Cited By:

[1] A. Elsidig, S. Babiker, "Rayleigh Fading Channel Equalization using Neural Networks", 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), p. 1, 2018


[2] D. Kapoor, A. Kohli, "Adaptive-Slope Squashing-Function-Based ANN for CSI Estimation and Symbol Detection in SFBC-OFDM System", Arabian Journal for Science and Engineering, 2021


[3] V. Jaswanth, S. Suryakala, S. Nikil, G. Dinesh Reddy, G. Trived Sai, "BER Analysis of Neural Equalizer in OFDM systems", IOP Conference Series: Materials Science and Engineering, Vol. 1130, p. 012056, 2021