The University of Southampton
University of Southampton Institutional Repository

Asymptotic Bayesian Decision Feedback Equalizer Using a Set of Hyperplanes

Chen, S., Mulgrew, B. and Hanzo, L. (2000) Asymptotic Bayesian Decision Feedback Equalizer Using a Set of Hyperplanes IEEE Transactions on Signal Processing, 48, (12), pp. 3493-3500.

Record type: Article


We present a signal space partitioning technique for realizing the optimal Bayesian decision feedback equalizer (DFE). It is known that, when the signal to noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. The proposed technique determines these hyperplanes explicitly and uses them to partition the observation signal space. The resulting equalizer is made up of a set of parallel linear discriminant functions and a Boolean mapper. Unlike the existing signal space partitioning technique of Kim and Moon, which involves complex combinatorial search and optimization in design, our design procedure is simple and straightforward, and guarantees to achieve the asymptotic Bayesian DFE.

PDF 00887042.pdf - Other
Download (187kB)

More information

Published date: December 2000
Organisations: Southampton Wireless Group


Local EPrints ID: 251151
ISSN: 1053-587X
PURE UUID: 1be729ea-0e26-4cde-9ede-44d386b0c71d

Catalogue record

Date deposited: 12 Jan 2004
Last modified: 18 Jul 2017 10:11

Export record


Author: S. Chen
Author: B. Mulgrew
Author: L. Hanzo

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.