## 1 Introduction  
The evolution of mobile communication systems has progressed from the first generation (IG) to the fifth generation (5G). In light of integrating the Internet of Things (IoT) in various domains such as smart factories, precision agriculture, transport systems, etc., optimizing connectivity is becoming of prime importance [1]. However, there are many challenges for mobile communication systems of the future to meet this trend. Knowing the path loss behavior of a communication channel is one among many challenging characteristics of wireless communications. Here, path loss (PL) refers to the decrease in reception over distance, which is dependent on many environmental factors [2]. Empirical models and ML models have been applied frequently to perform PL predictions. Empirical models use empirical data to establish a relationship between the signal strength at a transmitter and a receiver based on features like distance, frequency, environmental conditions, and obstacles [3]. These models are transparent predictors as they incorporate a small number of parameters, and the equations of these models are concise. Machine learning modelsare alternative models to analyze vast amounts of empirical data to identify complex patterns and dependencies that may not be easily captured by empirical models [3]. [PERSON] et al. have demonstrated that ML models are more accurate than empirical models [3]. Though, ML models, despite being highly accurate, are not sufficiently interpretable (or explainable) [4]. In recent research, explainable artificial intelligence (XAI) has received high attention to address the interpretability of ML models. Interpretability is defined by [PERSON] [4] as \"the degree to which a human can understand the cause of a decision\". The terms explainability and interpretability are often used interchangeably. Explainability is also referred to in the literature as interpretability, intelligibility, causability, or understandability [5; 6; 7]. Extension of XAI to the PL predictions made by ML models in wireless communication remains a research gap in the existing literature. In our work we propose to integrate accuracy and transparency in PL predictions by applying intrinsically interpretable ML models. Intrinsically interpretable ML models are glass box models per se due to their intrinsic structure [8]. In order to convey a comparative understanding, we utilize three categories of models, each of them represented by three concrete models to predict measured PL data from a university campus environment [9; 10]. We utilize empirical PL prediction models (represented in Section 4.1 by Okumura-Hata, COST-231 Hata, and ECC-33 models), ML-based PL prediction models (represented in Section 4.2 by random forest (RF), extreme gradient boosting (XGB), and multi-layer perception (MLP)), and glass box ML-based PL prediction models (represented in Section 4.3 by generalized additive model (GAM), generalized neural additive model (GNAM), and explainable boosting machine (EBM)). As visualized in Figure 1, our work contributes to the existing literature of PL models by demonstrating the high prediction accuracy of glass box ML models (compared to the empirical PL models and black box ML models), beside showing the advantage of glass box ML models to have intrinsic explanation power in order to shed light on the rationale behind the ML model predictions. Thereby, and to the best of our knowledge, our paper is the first to demonstrate the possible robustness of applying glass box ML models in PL prediction modeling. Figure 1: Model prediction accuracy versus model prediction transparency by different PL modeling approaches. The remainder of this paper is structured as follows: Section 2 provides a literature review of explainable ML models of PL prediction. Section 3 provides insight into the data used in our study. Section 4 concisely introduces the prediction models applied to the data, consisting of empirical models, black box ML, and glass box ML models.