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Optimization of the Training Symbols for Minimum Mean Square Error Equalizer

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Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016 (AECIA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 565))

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Abstract

The theory of Minimum Mean Square Error (MMSE) and Symbol Error Rate (SER) will be introduced and used as a parameter of analysis, we will find the optimized number of training symbols for different amounts of data. The training symbols are used in adaptive channel equalization where the communication channel is totally unknown, the training symbols are the data sent via the channel, the receiver already know which symbols it will receive, this way the equalizer can analyse the unknown channel and configure it’s coefficients to improve the communication. Simulations of a communication channel made in Matlab together with the parameter SER will show the optimized settings for different amounts of numbers of symbols for different values of Eb/E0 (the energy per bit to noise power spectral density ratio). After the simulations results, the settings will be implemented in a real hardware device (NI RF VSG PXI-5670 Vector Signal Generator and NI RF PXI VSA 5661 Vector Signal Analyzer) and the concepts of Modulation Error Ratio (MER) and Additive White Gaussian Noise (AWGN) will be used to evaluate the communication. The main purpose of this paper is verifying the theoretical assumptions concerning the impact of the number of training symbols on the quality of channel equalization in case of a real hardware in the form of software-defined radio (SDR). The real experiments brought the unique results, which can be used for the implementation of the feed-forward software defined equalization.

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Acknowledgements

This paper has been elaborated within the framework of the project SP2016/146 of the Student Grant System, VSB-TU Ostrava, Czech Republic.

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Authors and Affiliations

  1. Department of Cybernetics and Biomedical Engineering, 17. Listopadu 15, Ostrava, 70833, Czech Republic

    Radek Martinek, Radana Kahankova & Jan Žídek

  2. Department of Industrial Informatics and Instrumentation, University Joseph Fourrier, Place Du Conseil National de la Résistance, 38400, Saint-Martin-d’Hères, France

    George Razera

Authors
  1. Radek Martinek
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  2. George Razera
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  3. Radana Kahankova
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  4. Jan Žídek
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Corresponding author

Correspondence to Radana Kahankova .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Department of Mathematics and Computer, Faculty of Sciences and Techniques, Settat, Morocco

    Abdelkrim Haqiq

  3. Information Technology Department, Faculty of Computers and Information, Giza, Egypt

    Aboul Ella Hassanien

  4. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  5. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

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Martinek, R., Razera, G., Kahankova, R., Žídek, J. (2018). Optimization of the Training Symbols for Minimum Mean Square Error Equalizer. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-60834-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60833-4

  • Online ISBN: 978-3-319-60834-1

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