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2019

A Graph Neural Network Approach for Scalable Wireless Power Control

Shen, Yifei, Shi, Yuanming, Zhang, Jun et al.

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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.

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