## 1 Introduction  
The emerging fifth-generation (5G) mobile communication systems are expected to bring a complete revolution in applications and experiences [1]. Mobile operators worldwide are racing to roll out 5G services. However, among the technical challenges, accurate channel models to predict path loss are vital for the design of 5G cellular communication systems. Many path loss models for wireless communication networks have been studied by prior researchers. [PERSON] et al. presented extensive driving test measurement data of radio wave propagation in a campus environment and analyzed the correlation between the path loss and terrain information [2]. For 5G networks, standardization organizations, such as the International Telecommunications Union Radiocommunication Sector, have conducted channel measurements in urban and suburban environments for path loss modeling [3]. [PERSON] et al. presented the comparison of 5G path loss models based on simulation results and evaluated the suitability of the models in different scenarios [4]. [PERSON] et al. presented the comparison of the alpha-beta-gamma (ABG) and close-in (CI) path loss models using measured data and ray-tracing techniques, with carrier frequencies ranging from 2 GHz to 73 GHz [5]. The authors found that the physically based CI model yielded smaller prediction errors, with a standard deviation ranging from about 6 dB to12 dB [5]. To improve the ABG model, a weighted ABG model was proposed in [6], which suitably integrated or combined different available datasets and obtained better results in terms of model accuracy. It was reported that the weighted ABG model could obtain better results, with a standard deviation of prediction errors ranging from 1.2 dB to 12.5 dB [6]. To predict the millimeter-wave path loss, probabilistic models were presented in [7]. Based on real-world 28 GHz and 73 GHz measurements, the authors obtained the line-of-sight (LOS) probabilities from ray-tracing techniques and proposed a hybrid path loss model, which was a weighted sum of LOS and non-line-of-sight (NLOS) propagation loss [7]. The above-mentioned models were built based on the traditional statistical approaches. Recently, the research community has migrated to data-dependent machine learning methods [8]. [PERSON] et al. presented the accuracy of path loss predictions using machine learning methods, including support vector regression, random forest, and K-nearest neighbor algorithms, for long-term evolution networks [9]. [PERSON] et al. proposed a deep-learning-based path loss model utilizing satellite images aided by a simple path loss model [10]. Additionally, a deep learning approach to model radio propagation behaviors from satellite images was proposed in [11], where the path loss exponent and shadowing factor for the entire coverage area were estimated. Furthermore, an enhanced convolutional-neural-network-based path loss model was presented for millimeter-wave propagations [12]. It was reported that the root-mean-square error of the prediction could be reduced to 8.59 dB [12]. In addition to point-to-point path loss prediction, [PERSON] et al. proposed a method to evaluate path loss distribution in the coverage area directly from satellite images based on deep learning approaches [13]. Although deep learning approaches are indeed powerful in tasks involving prediction or classification, they often lack transparency [14]. The black-box nature of deep learning approaches decreases the insight and trust people have in the optimized modules. Therefore, explainable artificial intelligence is currently one of the most popular research topics [15]. The Taiwanese government completed its initial 5G spectrum auction at the beginning of 2020, and telecom operators are currently rolling out 5G services. The spectrum on offer covers 270 MHz in the 3.5 GHz band, 2500 MHz in the 28 GHz band, and 20 MHz in the 1.8 GHz band. In practice, mobile operators tend to roll out the initial 5G deployments in urban cities.