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 graph-structured 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 auto-encoder, 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.
翻译:随着计算机计算能力的迅速增强,在无线网络中广泛应用了深层次的学习方法,例如神经网络、经常神经网络等,这些方法在无线网络中广泛应用,并取得了令人印象深刻的性能。近年来,为了清除无线网络和背景信息中图形结构数据的地形信息,引入了图形神经网络,并实现了一系列无线网络问题的最新性能。在本次审查中,我们首先简单介绍了一些典型模式的进展,例如图象神经网络、图形关注网络、图表自动编码网络、图表经常网络、图表强化学习和空间时空图神经网络,以及图形神经网络。随后,详细讨论了无线网络中图形神经网络的若干应用,如电源控制、链接时间安排、频道控制、无线通信预测、定位云等。最后,讨论了关于无线网络中图形神经网络应用的一些研究趋势。