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Geometric Approach of Blind Channel Estimation

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Innovation and Interdisciplinary Solutions for Underserved Areas (CNRIA 2017, InterSol 2017)

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Abstract

This paper introduces a geometric approach of channel estimation (GACE). It is a blind channel estimation method for multiple input multiple output systems. GACE is based on a two-step geometric approach of source separation (GASS) that outperforms the existing ones. It is an approximated maximum likelihood estimation method which proceeds by the determination of the polyhedral edges tilts representing the matrix parameters. It operates by identifying matrix parameters using a geometric consideration depending on the probabilistic hypothesis of the sources. The simplicity of this method is based on a cloud observation, which is used to determine the edge of parallelogram describing the matrix channel parameters. In this paper, the case of real channel parameters and complex data sources for higher modulation order are performed. The simulation results show the efficiency of the proposed approach.

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Authors and Affiliations

  1. Ecole Supérieure Polytechnique, Université Cheikh Anta Diop, Dakar, Senegal

    Agbeti Bricos Ahossi & Roger Marcelin Faye

  2. Ecole Supérieure Multinationale des Télécommunications, 10 000, Dakar, Senegal

    Ahmed Dooguy Kora

Authors
  1. Agbeti Bricos Ahossi
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  2. Ahmed Dooguy Kora
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  3. Roger Marcelin Faye
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Corresponding author

Correspondence to Ahmed Dooguy Kora .

Editor information

Editors and Affiliations

  1. Université Cheikh Anta Diop, Dakar, Senegal

    Cheikh M. F. Kebe

  2. University of Maryland, College Park, College Park, Senegal

    Assane Gueye

  3. Université Assane Seck de Ziguinchor, Ziguinchor, Senegal

    Ababacar Ndiaye

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ahossi, A.B., Kora, A.D., Faye, R.M. (2018). Geometric Approach of Blind Channel Estimation. In: M. F. Kebe, C., Gueye, A., Ndiaye, A. (eds) Innovation and Interdisciplinary Solutions for Underserved Areas. CNRIA InterSol 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-319-72965-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-72965-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72964-0

  • Online ISBN: 978-3-319-72965-7

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