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Energy detector based TOA estimation for MMW systems using machine learning

Author

Listed:
  • Xiaolin Liang

    (Ocean University of China)

  • Hao Zhang

    (Ocean University of China
    University of Victoria)

  • Tingting Lu

    (Ocean University of China)

  • T. Aaron Gulliver

    (University of Victoria)

Abstract

60 GHz millimeter wave signals can provide precise time and multipath resolution and so have great potential for accurate time of arrival (TOA) and range estimation. To improve TOA estimation, a new energy detector based threshold selection algorithm which employs a neural network is proposed. The minimum slope, kurtosis, and skewness of the received energy block values are used to determine the normalized thresholds for different signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.

Suggested Citation

  • Xiaolin Liang & Hao Zhang & Tingting Lu & T. Aaron Gulliver, 2017. "Energy detector based TOA estimation for MMW systems using machine learning," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(2), pages 417-427, February.
    • Handle: RePEc:spr:telsys:v:64:y:2017:i:2:d:10.1007_s11235-016-0182-2
    DOI: 10.1007/s11235-016-0182-2
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