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8 June 2024 Machine learning-based classification method for millimeter wave indoor channel at 28 GHz
Youqiang Xu, Rongchen Sun
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Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 1317123 (2024) https://doi.org/10.1117/12.3031962
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China
Abstract
Accurate identification of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions can enhance the precision of indoor positioning. This paper proposes a method for identifying LOS and NLOS channel states in millimeter-wave indoor wireless positioning based on machine learning. In this approach, we introduce angular and frequency domain features for the first time and combine them with traditional channel characteristics to improve the accuracy of millimeter-wave indoor LOS/NLOS scene classification. The method utilizes an artificial neural network to analyze five distinct channel indicators extracted from the spatial, temporal, and frequency domains: the angular difference of the strongest path, maximum received power, average excess delay, root mean square delay spread, and the kurtosis of the frequency domain transfer function. Simulation results show that this method achieves an accuracy rate of 97.58%.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Youqiang Xu and Rongchen Sun "Machine learning-based classification method for millimeter wave indoor channel at 28 GHz", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 1317123 (8 June 2024); https://doi.org/10.1117/12.3031962
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KEYWORDS
Non line of sight propagation
Artificial neural networks
Machine learning
Extremely high frequency
Scene classification
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