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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.

Item Type:	Refereed Article
Keywords:	turbo equalizer, Hopfield neural network, Rayleigh fading, low complexity equalization
Research Division:	Engineering
Research Group:	Communications engineering
Research Field:	Signal processing
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in engineering
UTAS Author:	Olivier, JC (Professor JC Olivier)
ID Code:	87586
Year Published:	2013
Deposited By:	Engineering
Deposited On:	2013-11-25
Last Modified:	2017-11-06
Downloads:	399 View Download Statistics