Machine learning-based classification method for millimeter wave indoor channel at 28 GHz

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%.