GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-dense D2D mmWave Networks

He, S.; S., Xiong,; Zhang, W.; Y., Yang,; J., Ren,; Huang, Y.

doi:10.48550/arxiv.2107.02412

Cited by 1 publication

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“…In ultra-dense D2D mmWave communication networks, S. He et al considered the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs [63]. The resulting optimization problem is formulated as an constrained integer programming problem that is nonconvex and NP-hard.…”

Section: B Link Schedulingmentioning

confidence: 99%

An Overview on the Application of Graph Neural Networks in Wireless Networks

He¹,

Xiong²,

Ou³

et al. 2021

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With the rapid enhancement of computer computing power, deep learning methods, e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance. In recent years, in order to mine the topology information of graphstructured data in wireless network as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems. In this review, we first simply introduce the progress of several classical paradigms, such as graph convolutional neural networks, graph attention networks, graph autoencoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively. Then, several applications of graph neural networks in wireless networks such as power control, link scheduling, channel control, wireless traffic prediction, vehicular communication, point cloud, etc., are discussed in detail. Finally, some research trends about the applications of graph neural networks in wireless networks are discussed.

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Section: B Link Schedulingmentioning

confidence: 99%