DocumentCode :
1410037
Title :
Asymptotic Bayesian decision feedback equalizer using a set of hyperplanes
Author :
Chen, Sheng ; Mulgrew, Bernard ; Hanzo, Lajos
Author_Institution :
Dept. of Electron. & Comput. Sci., Southampton Univ., UK
Volume :
48
Issue :
12
fYear :
2000
fDate :
12/1/2000 12:00:00 AM
Firstpage :
3493
Lastpage :
3500
Abstract :
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 (1998), which involves complex combinatorial search and optimization in design, our design procedure is simple and straightforward, and guarantees to achieve the asymptotic Bayesian DFE.
Keywords :
Bayes methods; decision feedback equalisers; piecewise linear techniques; Boolean mapper; DFE; SNR; asymptotic Bayesian decision feedback equalizer; asymptotically piecewise linear case; decision boundary; design procedure; hyperplanes; observation signal space; parallel linear discriminant functions; signal space partitioning technique; signal-to-noise ratio; Bayesian methods; Bit error rate; Decision feedback equalizers; Detectors; H infinity control; Maximum likelihood estimation; Moon; Neural networks; Piecewise linear techniques; Signal design;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.887042
Filename :
887042
Link To Document :
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