LOS/NLOS Channel Identification Technology Based on CNN

In the field of indoor positioning, ultra-wideband (UWB) communication technology is the key technology. But the errors caused by non-line of sight (NLOS) communication channels could greatly affect the positioning accuracy. Therefore, identifying the channel type can effectively avoid the above-men...

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Bibliographic Details
Published in2019 6th NAFOSTED Conference on Information and Computer Science (NICS) pp. 200 - 203
Main Authors Wang, Fei, Xu, Zhan, Zhi, Ruxin, Chen, Jinhui, Zhang, Peiyue
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Channel models
CNN
Computer science
Delays
IEEE 802.15 Standard
Impulse Response
Indoor Positioning
NLOS
Support vector machines
Training
UWB
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Summary:In the field of indoor positioning, ultra-wideband (UWB) communication technology is the key technology. But the errors caused by non-line of sight (NLOS) communication channels could greatly affect the positioning accuracy. Therefore, identifying the channel type can effectively avoid the above-mentioned errors, thereby helping to reduce the loss of positioning accuracy. This paper proposes a LOS/NLOS channel identification method by using a convolutional neural network (CNN) to identifying the impulse response figures of the channels. This method is capable of identifying four different types of channel impulse response figures and providing excellent identification accuracy. The results show that the accuracy of identifying LOS (0-4m), NLOS (0-4m), NLOS (4-10m) and extreme NLOS channel impulse response figures can reach 98.24%.
DOI:10.1109/NICS48868.2019.9023805