Radial Transformation for Enhanced Deep Learning-Based Path Loss Map Prediction

Abstract

In recent deep learning-based path loss prediction methods, path loss map generation is commonly formulated as an image-to-image translation task. Most state-of-the-art methods in this field incorporate convolutional layers into their deep learning architectures. However, standard convolutional layers often struggle to effectively model the inherent radial nature of radio signal propagation due to the rectangular sliding mechanism of convolutional filters. In this paper, a radial transformation of the input data is proposed in order to better align the propagation geometry in path loss maps with the convolutional sliding process. Applying the proposed radial transformation to the input data reorganizes their spatial representation, enabling the model to learn propagation patterns more efficiently. Experimental results obtained across several state-of-the-art path loss map prediction methods demonstrate that the proposed transformation can achieve up to 25% reduction in root mean square error. Consequently, the proposed approach can serve as a computationally efficient alternative to deterministic ray-tracing-based models in applications where both prediction accuracy and computational efficiency are of primary importance.