A graph neural network approach for scalable wireless power control

Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

45 Citations (Scopus)


Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in K-user interference channels. Specifically, a K-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one- layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE.

Original languageEnglish
Title of host publication2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109602
Publication statusPublished - 9 Dec 2019
Event2019 IEEE Globecom Workshops, GC Wkshps 2019 - Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019

Publication series

Name2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings


Conference2019 IEEE Globecom Workshops, GC Wkshps 2019
Country/TerritoryUnited States


  • Geometric deep learning
  • Graph neural networks
  • Resource allocation
  • Wireless networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Control and Optimization


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