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In this article, a general model for non-iterative joint equalization and decoding is systematically derived for use in systems transmitting convolutionally encoded BPSK-modulated information through a multipath channel, with and without interleaving. Optimal equalization and decoding are discussed first, by presenting the maximum likelihood sequence estimation and maximum a posteriori probability algorithms and relating them to equalization in single-carrier channels with memory, and to the decoding of convolutional codes. The non-iterative joint equalizer/decoder (NI-JED) is then derived for the case where no interleaver is used, as well as for the case when block interleavers of varying depths are used, and complexity analyses are performed in each case. Simulation results are performed to compare the performance of the NI-JED to that of a conventional turbo equalizer (CTE), and it is shown that the NI-JED outperforms the CTE, although at much higher computational cost. This article serves to explain the state-of-the-art to students and professionals in the field of wireless communication systems, presenting these fundamental topics clearly and concisely.

Item Type:	Refereed Article
Keywords:	Hopfield neural networks, equalizer, decoder, joint equalization/decoding, computational complexity
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:	87585
Year Published:	2013
Deposited By:	Engineering
Deposited On:	2013-11-25
Last Modified:	2017-11-06
Downloads:	473 View Download Statistics