5GNN: extrapolating 5G measurements through GNNs

Wei Ye, Xinyue Hu, Tian Liu, Ruoyu Sun, Yanhua Li, Zhi-Li Zhang

Abstract: 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.