Repository for the ICASSP paper "GRAPH NEURAL NETWORKS IN LARGE SCALE WIRELESS COMMUNICATION NETWORKS: SCALABILITY ACROSS RANDOM GEOMETRIC GRAPHS".
You can clone the repository as is usually done:
git clone https://github.com/romm32/rgg_transferability.git
We provide a .yml file to set up a conda environment in Ubuntu 22, with which the installation of the packages should become easier.
The file data_generation enables generating a dataset. After this, you can run the main file inside the conda environment as follows.
python main.py
You can also specify training/evaluation parameters as arguments. You can request help via an email to rominag@seas.upenn.edu.
Please cite the papers if you use the code:
@INPROCEEDINGS{11460522,
author={Camargo, Romina Garcia and Wang, Zhiyang and Ribeiro, Alejandro},
booktitle={ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Graph Neural Networks in Large Scale Wireless Communication Networks: Scalability Across Random Geometric Graphs},
year={2026},
volume={},
number={},
pages={616-620},
keywords={Radio broadcasting;Frequency modulation;Filtering;Filters;Circuits and systems;Wireless communication;Wireless networks;Communication networks;Communication systems;Network architecture;transferability;graph neural networks;random geometric graphs},
doi={10.1109/ICASSP55912.2026.11460522}}