## 1 Introduction  
In 5G communication, millimeter wave (mmWave) communication is one of the most important technologies. Despite technical advantages, such as wide bandwidth and low latency, it suffers from low spectral efficiency, due to high sensitivity and low scattering and diffraction amounts, narrow coverage, and the unfavorable influence of non-line-of-sight (NLoS) propagation [1]. Thus, mmWave path loss modeling acts as a crucial factor in determining the optimal placement and configuration of 5G base stations, which are directly related to the quality of user experience. To this end, it is important to design a robust and accurate path loss model even in a complex propagation environment, such as a dense urban scenario. The existing path loss modeling methods are grouped into three categories: empirical methods [2, 3], deterministic methods [4, 5], and machine learning-based methods [6, 7, 8, 9]. The empirical method builds a model based on curve fitting methods by using measurement data collected in a typical path loss environment. In empirical models, the carrier frequency, the distance between the transmitter (Tx) and the receiver (Rx), and the path loss factor (PLE) are included as key parameters. They are often unsuitable, though, for cell planning and optimization purposes, due to limited prediction accuracy [2, 3]. Three-dimensional, 3D, ray-tracing is one of the most advanced deterministic mmWave path loss modeling methods. It simulates the behavior of physical propagation, based on geometric optics, including attenuation, reflection and scattering. The deterministic methods use a large number of computational resources, but can achieve higher accuracy than empirical methods. However, 3D models must be able to perform predictions precisely in propagation environments that are rarely used in deterministic methods in many deployment scenarios. Path loss modeling, based on machine learning, is treated as a regression problem based on measured data. In this path loss modeling, the characteristics, extracted according to the measurement location and propagation environment data, are used as input, and the measured path loss value is used as the desired answer in training. The characteristics of machine learning-based models for path loss in urban scenarios include those associated with rooftop diffraction, T-R separation, obstacle penetration, reflection, and characteristics of the transmitting antenna, etc. This allows neural networks to learn the relationship between inputs and outputs and, thus, generally outperforms empirical models. However, machine learning-based modeling requires more than a certain amount of domain knowledge in parts such as input preprocessing, feature selection and extraction, and may require a large amount of computation. Recently, a deep learning-based method for path loss modeling of mmWave in suburban and indoor environments has been proposed [10; 11]. It uses a two-dimensional image generation algorithm called the Local Area Multi-Line Scan (LAMS) algorithm to transform the 2D map data between Tx and Rx into a two-dimensional image containing topographic information for training convolutional neural networks (CNNs). These methods have shown promising results with better prediction performance, in terms of accuracy, than empirical and deterministic methods. However, 2D CNN-based modeling is not suitable for urban environments, where the heights and sizes of buildings are relatively irregular, because 2D images are disadvantageous in representing the different heights and detailed shapes of many buildings and complex networks of streets. Therefore, a three-dimensional LAMS image, which is advantageous for representing complex urban scenarios with many tall buildings, has been proposed to train 3D CNN, and the 3D CNN-based model has shown better performance, compared to the 2D CNN-based model [12].