5GNN: extrapolating 5G measurements through GNNs
5GNN: extrapolating 5G measurements through GNNs
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DOI：
10.1145/3565473.3569186
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Wei Ye;Xinyue Hu;Tian Liu;Ruoyu Sun;Yanhua Li;Zhi-Li Zhang
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文献类型：
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作者：
Wei Ye;Xinyue Hu;Tian Liu;Ruoyu Sun;Yanhua Li;Zhi-Li Zhang
5G网络的出现吸引了一系列测量研究，以了解它们在各种环境下的性能。遗憾的是，深入开展5G测量研究既费力又费钱。测量样本仅覆盖一个或多个5G塔台/基站的(可能很大的)覆盖区域中的有限点。在本文中，我们解决了以下基本问题：给定在目标5G覆盖区域内有限位置收集的5G“信号”测量的集合，我们能否推断或外推该区域内其他位置的5G“信号”，即我们没有样本？我们提出了一种新的基于图神经网络(GNN)的学习范式，称为5GNN，它基于测量的数据点来捕捉5G信号底层空间相关性的“局部”和“全局”模式。这一范式是由对5G网络物理特征的见解指导的。我们使用合成和真实世界的数据集进行全面的实验和评估，这些数据集是我们自己用专业工具收集和处理的。与使用现有GNN的基线模型相比，5GNN具有更好的性能，可以将信号估计任务和信道质量回归任务的估计误差分别降低12.8%和9.2%。
The advent of 5G networks has attracted a flurry of measurement studies to understand their performance in various settings. Unfortunately, carrying out an in-depth measurement study of 5G is both laborious and costly. The measurement samples cover only limited points in a (potentially large) coverage area of one or more 5G towers/base stations. In this paper, we tackle the following basic question: given a collection of 5G "signal" measurements collected in limited locations in a target 5G coverage area, can we infer or extrapolate 5G "signals" at other locations within the area that we do not have samples? We propose a novel learning paradigm based on graph neural networks (GNNs), dubbed 5GNN, which captures both the "local" and "global" patterns of the underlying spatial correlation of 5G signals based on the measured data points. This paradigm is guided by insights from the physical characteristics of 5G networks. We conduct comprehensive experiments and evaluations using both synthetic and real-world datasets, which are collected and processed by ourselves with professional tools. Compared with baseline models using existing GNNs, 5GNN is superior and can reduce the estimation errors for the signal imputation task and channel quality regression task by up to 12.8% and 9.2%, respectively.