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This repository contains our work<br /> Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air, which is accepted by the TWC.<br />

For any reproduce, further research or development, please kindly cite our paper<br /> @ARTICLE{GNN_aggregation_OTA,<br /> author={Gu, Yifan and She, Changyang and Quan, Zhi and Qiu, Chen and Xu, Xiaodong},<br /> journal={IEEE Transactions on Wireless Communications}, title={Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air},<br /> year={2023},<br /> volume={22},<br /> number={11},<br /> pages={7551-7564}, <br /> month={Nov.},<br /> }<br />

Instructions:<br />

1. Simulation for MPNN, WMMSE and EPA policies can be found in MPNN and WMMSE and EPA.py.<br />
2. Simulation for the proposed Air-MPNN can be found in Air-MPNN.py.<br />
3. Simulation for the proposed Air-MPRNN can be found in Air-MPRNN.py.<br />
4. We give examples for scalability and signaling overhead simulations.<br /> To consider different link densities for testing, change the parameter filed_length in the line test_config.field_length = field_length.<br /> To consider different channel correlation coefficient for testing, change the parameter r in the helper_functions.py.<br />

We thank the works "Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis" and "Spatial Deep Learning for Wireless Scheduling" for their source codes in creating this repository.