Unsupervised Graph Coloring using Gated Graph Neural Network

In this repo, we show the unsupervised graph coloring method using gated graph neural network applied for frequency allocation in wireless multicellular system in the paper D. Abode, R. Adeogun, L. Salaün, R. Abreu, T. Jacobsen and G. Berardinelli, "Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks," 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Washington, DC, USA, 2024, pp. 1-6, doi: 10.1109/VTC2024-Fall63153.2024.10757647

Please note the requirements.txt file, which defines the environment required to run this code

If you find the code useful, kindly cite the corresponding paper -

Citation

@INPROCEEDINGS{10757647,\
  author={Abode, Daniel and Adeogun, Ramoni and Salaün, Lou and Abreu, Renato and Jacobsen, Thomas and Berardinelli, Gilberto}, \
  booktitle={2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)}, \
  title={Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks}, \
  year={2024}, \
  pages={1-6}, \
  doi={10.1109/VTC2024-Fall63153.2024.10757647}\
  }

Instructions for setting up the environment and installing dependencies

1. Create a new conda environment (replace myenv with your desired environment name) You can specify Python version if needed, for example, python=3.11 for Python 3.11 conda create -n myenv python=3.11

2. Activate the conda environment conda activate myenv

3. Install dependencies using pip Ensure pip is up-to-date in the environment: conda install pip

4. Install the required packages from this file pip install -r requirements.txt

networkx==3.1 torch==2.1.0 torch-geometric==2.5.2 numpy==1.26.0 matplotlib