Fadhil, DiyarOliveira, Rodolfo2023-03-142023-03-142022-12-122073-431XPURE: 55635820PURE UUID: a550854e-2cc0-4cb9-951d-5314dbe33c91Scopus: 85144736485WOS: 000900481000001http://hdl.handle.net/10362/150558Publisher Copyright: © 2022 by the authors.Network analytics provide a comprehensive picture of the network’s Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the Core and the Radio Access Network (RAN) E2E delay of 5G networks with the Standalone (SA) and Non-Standalone (NSA) topologies when a single known Probability Density Function (PDF) is not suitable to model its distribution. To this end, multiple PDFs, denominated as components, are combined in a Gaussian Mixture Model (GMM) to represent the distribution of the E2E delay. The accuracy and computation time of the GMM is evaluated for a different number of components and a number of samples. The results presented in the paper are based on a dataset of E2E delay values sampled from both SA and NSA 5G networks. Finally, we show that the GMM can be adopted to estimate a high diversity of E2E delay patterns found in 5G networks and its computation time can be adequate for a large range of applications.2111792719engcellular networksend-to-end delayGaussian mixture modelquality of serviceHuman-Computer InteractionComputer Networks and Communicationsjournal article10.3390/computers11120184https://www.scopus.com/pages/publications/85144736485