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A low complexity Hopfield neural network turbo equalizer

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journal contribution
posted on 2023-05-17, 20:41 authored by Myburgh, HC, Jan OlivierJan Olivier
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.

History

Publication title

EURASIP Journal on Advances in Signal Processing

Volume

15

Pagination

1-22

ISSN

1687-6180

Department/School

School of Engineering

Publisher

Springer

Place of publication

Europe

Rights statement

Copyright 2013 Myburgh and Olivier

Repository Status

  • Open

Socio-economic Objectives

Expanding knowledge in engineering

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