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Conference Papers Year : 2022

Estimation of the End-to-End Delay in 5G Networks Through Gaussian Mixture Models

Abstract

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 E2E delay of heterogeneous networks when a single known probabilistic density function (PDF) is not adequate to model its distribution. To this end, multiple PDFs, denominated as components, are assumed 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. The results presented in the paper consider a dataset containing E2E delay traces sampled from a 5G network, showing that the GMM’s accuracy allows addressing the rich diversity of probabilistic patterns found in 5G networks and its computation time is adequate for real-time applications.
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hal-04308396 , version 1 (27-11-2023)

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Diyar Fadhil, Rodolfo Oliveira. Estimation of the End-to-End Delay in 5G Networks Through Gaussian Mixture Models. 13th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Jun 2022, Caparica, Portugal. pp.83-91, ⟨10.1007/978-3-031-07520-9_8⟩. ⟨hal-04308396⟩
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