LIANG Xiaolin, JIN Yiheng, ZHANG Hao, LYU Tingting. NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System[J]. Chinese Journal of Electronics, 2018, 27(1): 175-182. doi: 10.1049/cje.2017.11.003
Citation: LIANG Xiaolin, JIN Yiheng, ZHANG Hao, LYU Tingting. NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System[J]. Chinese Journal of Electronics, 2018, 27(1): 175-182. doi: 10.1049/cje.2017.11.003

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

doi: 10.1049/cje.2017.11.003
Funds:  This work is supported by the National Natural Science Foundation of China (No.61501424, No.61701462, No.41527901), Ao Shan Science and Technology Innovation Project of Qingdao National Laboratory for Marine Science and Technology (No.2017ASKJ01), Qingdao Science and Technology Plan (No.17-1-1-7-jch), Fundamental Research Funds for the Central Universities (No.201713018), National High Technology Research and Development Program of China (No.2012AA061403), and the National Science and Technology Pillar Program during the Twelfth Five-year Plan Period (No.2014BAK12B00).
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  • Corresponding author: LYU Tingting (corresponding author) was born in Shandong, China, in 1983. She received the B.S degree in communication engineering in 2006 from HuNan University. Then she got the M.S. degree in communication and information systems in 2009, and Ph.D degree in computer application technology in 2013 from Ocean University of China. Now she is a lecturer at Ocean University of China. Her research interests are mmWave communication systems, ultrawideband radio systems, OFDM technologies and MIMO wireless systems. (Email:lvtingting33@163.com)
  • Received Date: 2015-11-16
  • Rev Recd Date: 2016-04-18
  • Publish Date: 2018-01-10
  • Millimeter-wave (MMW) signals in 60GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight (NLOS) propagation is a primary factor affecting the range precision. To improve range estimation, an Energy detector (ED) based normalized threshold algorithm which employs a Neural network (NN) is developed on the basis of NLOS identification. The maximum curl and standard deviation of the received energy block values are used to determine NLOS environment and 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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