Automated Modeling Method for Pathloss Model Discovery

TL;DR

This paper introduces an automated, interpretable AI-based method for discovering accurate path loss models in wireless communication, outperforming traditional techniques in prediction accuracy and efficiency.

Contribution

It presents a novel automated approach using Deep Symbolic Regression and Kolmogorov-Arnold Networks for interpretable path loss modeling, enhancing accuracy and discovery speed.

Findings

Kolmogorov-Arnold Networks achieve R^2 close to 1 with minimal error.

Deep Symbolic Regression produces compact, moderately accurate models.

Automated methods outperform traditional models with up to 75% error reduction.

Abstract

Modeling propagation is the cornerstone for designing and optimizing next-generation wireless systems, with a particular emphasis on 5G and beyond era. 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 interpretability of modeling. Artificial intelligence (AI)-based techniques, in particular, are increasingly being adopted to overcome this challenge, although the interpretability is not assured with most of these methods. Inspired by recent advancements in AI, this paper proposes a novel approach that accelerates the discovery of path loss models while maintaining interpretability. 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, offering full interpretability, and the second based on Kolmogorov-Arnold Networks, providing two levels of interpretability. Both approaches are evaluated on two synthetic and two real-world datasets. Our results show that Kolmogorov-Arnold Networks achieve the coefficient of determination value R^2 close to 1 with minimal prediction error, while Deep Symbolic Regression 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 explainable solutions with potential to increase the efficiency of discovering next-generation path loss models.