Reducing Bayes equalizer complexity: a new approach for clusters determination

A new strategy for channel equalization in digital communication is presented. In this approach, the clustering problem is treated analytically. We propose a systematic Bayesian classification using a Gaussian approximation of the probability density function for each cluster. The quality of the app...

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Bibliographic Details
Main Authors: Montalvao F., J.R., Mota, J.C.M., Dorizzi, B., Cavalcante, C.C.
Format: Conference Proceeding
Language:English
Subjects:
Additive noise
Bayesian methods
Context
Digital communication
Equalizers
Finite impulse response filter
Gaussian noise
Intersymbol interference
Maximum likelihood detection
Performance analysis
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Summary:A new strategy for channel equalization in digital communication is presented. In this approach, the clustering problem is treated analytically. We propose a systematic Bayesian classification using a Gaussian approximation of the probability density function for each cluster. The quality of the approximation depends on the number of clusters considered. We show analytically that we can obtain the Bayes equalizer performance if we use the maximum number of clusters and we can obtain the Wiener equalizer performance if we use only two clusters (binary signal case). Some computational simulations illustrate the power of the presented strategy.
DOI:10.1109/ITS.1998.718431