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Open Access Research

A low complexity Hopfield neural network turbo equalizer

Hermanus C Myburgh1* and Jan C Olivier2

Author Affiliations

1 Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, 0002, South Africa

2 School of Engineering, University of Tasmania, Hobart, 7001, Australia

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EURASIP Journal on Advances in Signal Processing 2013, 2013:15  doi:10.1186/1687-6180-2013-15

Published: 8 February 2013


In this article, it is proposed that a Hopfield neural network (HNN) can be used to jointly equalize and decode information transmitted over a highly dispersive Rayleigh fading multipath channel. It is shown that a HNN MLSE equalizer and a HNN MLSE decoder can be merged in order to realize a low complexity joint equalizer and decoder, or turbo equalizer, without additional computational complexity due to the decoder. The computational complexity of the Hopfield neural network turbo equalizer (HNN-TE) is almost quadratic in the coded data block length and approximately independent of the channel memory length, which makes it an attractive choice for systems with extremely long memory. Results show that the performance of the proposed HNN-TE closely matches that of a conventional turbo equalizer in systems with short channel memory, and achieves near-matched filter performance in systems with extremely large memory.

Turbo equalizer; Hopfield neural network; Rayleigh fading; Low complexity