Limitations of Gaussian Mixture Models for Symbol Detection in Harsh Electromagnetic Environments

This letter presents a study on the limitations of Gaussian mixture models (GMMs) for symbol detection in digital communication systems. Although GMMs have proven to be adequate for symbol detection at medium signal-to-interference ratios, the results of this study reveal a critical flaw in the conventional assumption that Gaussian models can accurately represent the received symbol distributions under all circumstances in harsh environments. This statistical approach proves to be inadequate at high levels of electromagnetic (EM) interference, resulting in an inability to accurately differentiate between symbols. The methodology is tested in a triple modular redundant system subjected to reverberant EM disturbances, with diverse PAM-4 as an encoding technique.

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Journal
IEEE Letters on Electromagnetic Compatibility Practice and Applications
Published
2024-11-22
DOI
https://doi.org/10.1109/lemcpa.2024.3504712
Citations
1
Primary Topic
Bayesian Methods and Mixture Models
Type
article
Field-Weighted Citation Impact
0.35

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article

Limitations of Gaussian Mixture Models for Symbol Detection in Harsh Electromagnetic Environments

Davy Pissoort, Dries Vanoost, Mathias Verbeke, Míriam González-Atienza
1 citations
IEEE Letters on Electromagnetic Compatibility Practice and Applications
Bayesian Methods and Mixture Models
0.35
article

Limitations of Gaussian Mixture Models for Symbol Detection in Harsh Electromagnetic Environments

Davy Pissoort, Dries Vanoost, Mathias Verbeke, Míriam González-Atienza
article en
1 citations

Abstract

This letter presents a study on the limitations of Gaussian mixture models (GMMs) for symbol detection in digital communication systems. Although GMMs have proven to be adequate for symbol detection at medium signal-to-interference ratios, the results of this study reveal a critical flaw in the conventional assumption that Gaussian models can accurately represent the received symbol distributions under all circumstances in harsh environments. This statistical approach proves to be inadequate at high levels of electromagnetic (EM) interference, resulting in an inability to accurately differentiate between symbols. The methodology is tested in a triple modular redundant system subjected to reverberant EM disturbances, with diverse PAM-4 as an encoding technique.

IEEE Letters on Electromagnetic Compatibility Practice and ApplicationsVol. 7(1)
Flanders Make (Belgium) (BE)
European Commission
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Openalex Percentile: Top 31%
Bayesian Methods and Mixture Models
0.35
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Limitations of Gaussian Mixture Models for Symbol Detection in Harsh Electromagnetic Environments — Davy Pissoort, Dries Vanoost, et al. · IEEE Letters on Electromagnetic Compatibility Practice and Applications (2024) | TGRS Research Map | TGRS