Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

@article{Shen2021GraphNN,
  title={Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis},
  author={Yifei Shen and Yuanming Shi and Jun Zhang and Khaled Ben Letaief},
  journal={IEEE Journal on Selected Areas in Communications},
  year={2021},
  volume={39},
  pages={101-115}
}
Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural… 

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