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Machine Learning Technique Based on Gaussian Mixture Model for Environment Friendly Communications

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Information Technology for Education, Science, and Technics (ITEST 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 178))

Abstract

A machine learning technique, which is based on the Gaussian mixture model and uses a developed parametric and criteria features modification of the expectation-maximization (EM) algorithm with removing components of the Gaussian mixture model for a deep statistical analysis of cross-correlations between code structures in low power environment friendly direct sequence spread spectrum (DSSS) non-orthogonal multiple access (NOMA) communications, is proposed in the paper. The features of the EM-algorithm for this purpose are described and analyzed. The proposed modification of the EM-algorithm contains the justification of the initial number of components of a mixture, the initial model parameters, and three additional clustering criteria for adjusting the procedures of EM-algorithm under conditions of mathematical singularities in the log-likelihood function. An example of working of the proposed technique for DSSS NOMA communications is presented and analyzed.

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Author information

Authors and Affiliations

  1. National Aviation University, Kyiv, Ukraine

    Oleksii Holubnychyi, Yevhen Gabrousenko, Anatolii Taranenko, Olexandr Slobodian & Olena Zharova

Authors
  1. Oleksii Holubnychyi
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  2. Yevhen Gabrousenko
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  3. Anatolii Taranenko
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  4. Olexandr Slobodian
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  5. Olena Zharova
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Corresponding author

Correspondence to Oleksii Holubnychyi .

Editor information

Editors and Affiliations

  1. Department of Information Security and Computer Engineering, Cherkasy State Technological University, Cherkasy, Ukraine

    Emil Faure

  2. Department of Computer science and Systems Analysis, Cherkasy State Technological University, Cherkasy, Ukraine

    Olena Danchenko

  3. Department of Instrumentation, Mechatronics and Computerized Technologies, Cherkasy State Technological University, Cherkasy, Ukraine

    Maksym Bondarenko

  4. Department of Computer science and Systems Analysis, Cherkasy State Technological University, Cherkasy, Ukraine

    Yurii Tryus

  5. Department of Instrumentation, Mechatronics and Computerized Technologies, Cherkasy State Technological University, Cherkasy, Ukraine

    Constantine Bazilo

  6. Department of Software of Automated Systems, Cherkasy State Technological University, Cherkasy, Ukraine

    Grygoriy Zaspa

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Holubnychyi, O., Gabrousenko, Y., Taranenko, A., Slobodian, O., Zharova, O. (2023). Machine Learning Technique Based on Gaussian Mixture Model for Environment Friendly Communications. In: Faure, E., Danchenko, O., Bondarenko, M., Tryus, Y., Bazilo, C., Zaspa, G. (eds) Information Technology for Education, Science, and Technics. ITEST 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-031-35467-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-35467-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35466-3

  • Online ISBN: 978-3-031-35467-0

  • eBook Packages: EngineeringEngineering (R0)

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