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Energy detector based TOA estimation for MMW systems using machine learning

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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.

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Acknowledgments

This work was supported by the Nature Science Foundation of China under Grant No. 41527901.

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Xiaolin Liang, Hao Zhang & Tingting Lu

  2. Department of Electrical Computer Engineering, University of Victoria, Victoria, V8P 5C2, Canada

    Hao Zhang & T. Aaron Gulliver

Authors
  1. Xiaolin Liang
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  2. Hao Zhang
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  3. Tingting Lu
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  4. T. Aaron Gulliver
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Correspondence to Xiaolin Liang.

Appendix

Appendix

In order to make the readers easy to read and understand the paper, Table 4 shows the full form of these indigestible abbreviations.

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Liang, X., Zhang, H., Lu, T. et al. Energy detector based TOA estimation for MMW systems using machine learning. Telecommun Syst 64, 417–427 (2017). https://doi.org/10.1007/s11235-016-0182-2

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  • DOI: https://doi.org/10.1007/s11235-016-0182-2

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