• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

Vol. 10, No. 7, July 30, 2016
10.3837/tiis.2016.07.010, Download Paper (Free):

Abstract

Neural networks (NNs) are extensively used in applications requiring signal classification and regression analysis. In this paper, a NN based threshold selection algorithm for 60 GHz millimeter wave (MMW) time of arrival (TOA) estimation using an energy detector (ED) is proposed which is based on the skewness, kurtosis, and curl of the received energy block values. The best normalized threshold for a given signal-to-noise ratio (SNR) is determined, and the influence of the integration period and channel on the performance is investigated. Results are presented which show that the proposed NN based algorithm provides superior precision and better robustness than other ED based algorithms over a wide range of SNR values. Further, it is independent of the integration period and channel model.


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Cite this article

[IEEE Style]
X. Liang, H. Zhang and T. A. Gulliver, "Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals," KSII Transactions on Internet and Information Systems, vol. 10, no. 7, pp. 3050-3065, 2016. DOI: 10.3837/tiis.2016.07.010.

[ACM Style]
Xiaolin Liang, Hao Zhang, and T. Aaron Gulliver. 2016. Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals. KSII Transactions on Internet and Information Systems, 10, 7, (2016), 3050-3065. DOI: 10.3837/tiis.2016.07.010.