Code implementation of our paper Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks.
envs.py
defines classes of heterogeneous wireless channels and provides an implementation of the closed-form FP algorithm in heterogeneous settings.gen_data.py
generates datasets for training/test.utils.py
includes functions shared by bothtrain_hignn.py
andtrain_dnn.py
.nn_modules.py
defines the neural network (NN) modules.train_hignn.py
is the main file carrying out the training-loop of heterogeneous interference graph neural networks (HIGNNs).train_dnn.py
is the main file carrying out the training-loop of deep neural networks (DNNs) as comparison.
If you use this code, please cite our work:
@INPROCEEDINGS{9685457,
author={Zhang, Xiaochen and Zhao, Haitao and Xiong, Jun and Liu, Xiaoran and Zhou, Li and Wei, Jibo},
booktitle={2021 IEEE Global Communications Conference (GLOBECOM)},
title={Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks},
year={2021},
volume={},
number={},
pages={01-06},
doi={10.1109/GLOBECOM46510.2021.9685457}
}
If you have any questions, please contact [email protected].