This repository contains a suite of Jupyter notebooks implementing automatic pathloss approximation using Kolmogorov-Arnold (KA) networks. KA networks are a class of neural networks particularly suited for function approximation. We explore their performance on both measured and synthetic pathloss data.
We study the feasibility of approximation using data generated with the existing ABG and CI models as well as with empirical measurements.
If you use this code in your research, citation of the following paper would be greatly appreciated:
@inproceedings{
anaqreh2024towards,
title={Towards Automated and Interpretable Pathloss Approximation Methods},
author={Ahmad Anaqreh and Shih-Kai Chou and Irina Bara{\v{s}}in and Carolina Fortuna},
booktitle={Submitted to AAAI 2025 Workshop on Artificial Intelligence for Wireless Communications and Networking (AI4WCN)},
year={2024},
url={https://openreview.net/forum?id=M1WT5NZ4bj}
}
@article{anaqreh2025automated,
title={Automated Modeling Method for Pathloss Model Discovery},
author={Anaqreh, Ahmad and Chou, Shih-Kai and Mohor{\v{c}}i{\v{c}}, Mihael and Fortuna, Carolina},
journal={arXiv preprint arXiv:2505.23383},
year={2025}
url={https://arxiv.org/pdf/2505.23383?}
}