Open AccessNLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System
Author(s) -
Liang Xiaolin,
Jin Yiheng,
Zhang Hao,
Lyu Tingting
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.11.003
Subject(s) - non line of sight propagation , ranging , computer science , multipath propagation , standard deviation , range (aeronautics) , energy (signal processing) , detector , algorithm , channel (broadcasting) , telecommunications , statistics , wireless , mathematics , engineering , aerospace engineering
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.