A graph neural network approach for scalable wireless power control

doi:10.1109/GCWkshps45667.2019.9024538

	A graph neural network approach for scalable wireless power control
	Shen, Yifei 1; Shi, Yuanming2 ; Zhang, Jun 3; Letaief, Khaled B.1
	2019
会议录名称	2019 IEEE GLOBECOM WORKSHOPS, GC WKSHPS 2019
发表状态	已发表
DOI	10.1109/GCWkshps45667.2019.9024538
摘要	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. © 2019 IEEE.
会议地点	Waikoloa, HI, United states
URL	查看原文
收录类别	EI ; CPCI-S ; CPCI
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20201308358778
EI主题词	Channel state information ; Convolution ; Deep learning ; Deep neural networks ; Image processing ; Multilayer neural networks ; Power control ; Resource allocation ; Scalability ; Signal interference ; Wireless networks
EI分类号	Information Theory and Signal Processing:716.1 ; Radio Systems and Equipment:716.3 ; Specific Variables Control:731.3 ; Management:912.2 ; Systems Science:961
原始文献类型	Conference article (CA)
引用统计
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/120822
专题	信息科学与技术学院_PI研究组_石远明组
通讯作者	Shen, Yifei
作者单位	1.Dept. of Ece, Hong Kong University of Science and Technology, Hong Kong 2.School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China 3.Dept. of Eie, Hong Kong Polytechnic University, Hong Kong
推荐引用方式 GB/T 7714	Shen, Yifei,Shi, Yuanming,Zhang, Jun,et al. A graph neural network approach for scalable wireless power control[C]:Institute of Electrical and Electronics Engineers Inc.,2019.