Automated Modeling Method for Pathloss Model Discovery

Modeling propagation is the cornerstone for designing and optimizing next-generation wireless systems, with a particular emphasis on 5G and beyond technologies, such as reconfigurable intelligent surfaces, terahertz (THz) communications, and beam steering in massive multi-input multi-output (mMIMO) systems. Traditional modeling methods have long relied on statistic-based techniques to characterize propagation behavior across different environments. With the expansion of wireless communication systems, there is a growing demand for methods that guarantee the accuracy and explicit functional interpretability of modeling. Artificial intelligence (AI)-based techniques, in particular, are increasingly being adopted to overcome this challenge. Inspired by recent advancements in AI, this paper proposes a novel approach that accelerates the discovery of pathloss (PL) models while maintaining interpretability, referring to explicit functional interpretability in the remainder of the paper unless otherwise stated. The proposed method automates the formulation, evaluation, and refinement of the model, facilitating the discovery of the model. We examine two techniques: one based on Deep Symbolic Regression (DSR), offering interpretability, and the second based on Kolmogorov-Arnold Networks (KANs), providing two levels of interpretability. Both approaches are evaluated on two synthetic and two real-world datasets. Our results show that KANs achieve the coefficient of determination value$R^{2}$close to 1 with minimal prediction error, while DSR generates compact models with moderate accuracy. Moreover, on the selected examples, we demonstrate that automated methods outperform traditional methods, achieving up to 75% reduction in prediction errors, offering accurate and functionally interpretable and analyzable solutions with potential to increase the efficiency of discovering next-generation pathloss models.