## 7 Conclusions  
While contributions of ML-driven PL models in the literature of communication systems are increasing, our paper, by means of a systematic literature research, shows that only a subset of the studies apply XAI to clarify the logic behind the corresponding ML models to predict PL. The literature analysis in this paper furthermore indicates that, while the usage of intrinsically interpretable XAI methods to understand the ML model's decision rationale is pivotal, it is not yet applied. This paper takes a first step to exemplify the feasibility of balancing the prediction accuracy and explanation power of PL modeling via inherently explainable ML models. In addition, transforming longitude-latitude information to polar coordinates is applied in this paper. We thereby disentangle the primary role of the Tx-Rx transmitting angle as the foremost significant feature to explain signal strengthening or weakening in the studied university campus area. Elevation of receiver locations and their distance from the transmitter are explained as the second and third influential features, in the underlying environment, respectively. In addition, the magnitude of the effects of each of the significant features on signal propagation in the underlying environment of our study is illuminated. In future research, the glass box models applied in our paper can be further examined by applying them to multiple further signal attenuation problems comprising multiple terrains, frequencies, and transceiver configurations. Also, the comparison to deterministic modeling via ray tracing (RT) techniques could be interesting. We have tested our models in this paper on synthetically generated data based on the empirical 3 GPP model, which demonstrates accurate and transparent results in the same way as shown in this paper (The code and the input data to replicate the 3 GPP model can be downloaded at the Supplementary Material of this paper). The results provide a foundational basis for our further research on examining the applied models in the implemented propagation environment and radio system setup in the project NoLa (Nomadic 5G Networks for Small-Scale Rural Areas [[https://nola-5g.de/](https://nola-5g.de/)]([https://nola-5g.de/](https://nola-5g.de/)), accessed on 20 February 2025). Thereby, we aim to explore multiple parameters, including signal strength, data throughput, latency, and packet loss. Adding additional information to the input part, e.g., the receiver points' degree of LoS or NLoS information, the terrain occupancy map, and vegetation distribution on the path between the transceivers, can enhance the efficiency of our modelling in the next step. The feasibility of applying GNAMs to incorporate the bilateral interactive effects of the influencing features (GA2 Ms), as well as higher orders of interactions within PL prediction environments, can be a further research subject. GNAMs can incorporate a variety of networks as alternatives to MLP, e.g., CNN, ResNet, RandomFourierNet, etc., as the default, which can render more computational flexibility in dealing with more complex tasks. **Supplementary Materials:** The supporting information including the data and code can be downloaded at: [[https://gitlab.uni-koblenz.de/hamedkhalili/nola](https://gitlab.uni-koblenz.de/hamedkhalili/nola)]([https://gitlab.uni-koblenz.de/hamedkhalili/nola](https://gitlab.uni-koblenz.de/hamedkhalili/nola)) (accessed on 25 March 2025). **Author Contributions:** Conceptualization, H.F., M.A.W. and H.K. ; methodology, H.F. and H.K. ; software, H.K. ; validation, M.A.W. and H.F.; formal analysis, H.K. ; investigation, H.K. ; resources, M.A.W. and H.F.; data curation, H.K. ; writing--original draft preparation, H.K. ; writing--review and editing, M.A.W. and H.F.; visualization, H.K. ; supervision, M.A.W. and H.F.; project administration, M.A.W. ; funding acquisition, M.A.W. All authors have read and agreed to the published version of the manuscript.