hignn

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 both train_hignn.py and train_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 zhangxiaochen14@nudt.edu.cn.