Path loss modelling based on path profile in urban propagation environments

Abstract

This paper presents a path loss model based on path profile in urban propagation environments for 5G systems. Although deep learning approaches are indeed powerful in tasks involving prediction or classification, they often lack transparency and suffer from high computational complexity. The proposed model combines the log-distance path loss model for line-of-sight propagation scenarios and a machine-learning-based model for non-line-of-sight (NLOS) cases. This paper uses the principal component analysis algorithm to extract relevant features out of some selected attributes of the path profile for NLOS cases. Then, the path loss model can be constructed based on the approach of polynomial regression. Simulation results show that the proposed model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by about 22.2–37.2% dB when compared to the conventional models. Furthermore, the prediction performance was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei city. The real-world measurements show that the standard deviation of prediction error can be reduced by 3.33–6.13 dB when compared to the conventional models.