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Observation Interval Analysis for Faster-Than-Nyquist Signals Coherent Detection with Decision Feedback

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2021, ruSMART 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 13158))

Abstract

The method of transmitting signals above the Nyquist limit seems to be promising in terms of increasing bandwidth efficiency, but its practical application is limited by the complexity of the implementation of receiver devices, which should apply complex detection algorithms to eliminate the negative effect of intersymbol interference in the signal. In this paper, we consider a simple algorithm of elementwise detection with decision feedback and its modification, which makes it possible to increase the detection BER performance without increasing the computational complexity. The modification involves the optimization on observation interval to minimize the interference of subsequent signals during detection procedures.

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Acknowledgements

The reported study was funded by RFBR according to the research project № 20-37-90007\20

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Ilya Lavrenyuk & Sergey Makarov

  2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Wei Xue

Authors
  1. Ilya Lavrenyuk
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  2. Sergey Makarov
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  3. Wei Xue
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Corresponding author

Correspondence to Ilya Lavrenyuk .

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Editors and Affiliations

  1. Tampere University, Tampere, Finland

    Yevgeni Koucheryavy

  2. FRUCT Oy, Helsinki, Finland

    Sergey Balandin

  3. Tampere University, Tampere, Finland

    Sergey Andreev

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Lavrenyuk, I., Makarov, S., Xue, W. (2022). Observation Interval Analysis for Faster-Than-Nyquist Signals Coherent Detection with Decision Feedback. In: Koucheryavy, Y., Balandin, S., Andreev, S. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2021 2021. Lecture Notes in Computer Science(), vol 13158. Springer, Cham. https://doi.org/10.1007/978-3-030-97777-1_36

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  • DOI: https://doi.org/10.1007/978-3-030-97777-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97776-4

  • Online ISBN: 978-3-030-97777-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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