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- Title
Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals.
- Authors
Xiaolin Liang; Hao Zhang; Gulliver, T. Aaron
- 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.
- Subjects
ARTIFICIAL neural networks; REGRESSION analysis; SIGNAL-to-noise ratio; COMPUTER algorithms; SKEWNESS (Probability theory); KURTOSIS
- Publication
KSII Transactions on Internet & Information Systems, 2016, Vol 10, Issue 7, p3050
- ISSN
1976-7277
- Publication type
Article
- DOI
10.3837/tiis.2016.07.010