@inproceedings{3e5a8aa727014726ac485c60b3200522,
title = "Efficient Prediction of End-to-End Delay Ranges in 5G Networks",
abstract = "Predicting End-to-End (E2E) delay is essential for optimizing performance and maintaining Quality of Service (QoS) in 5G networks. This paper proposes a methodology to predict E2E delay ranges in 5G, based on the Hidden Markov Model (HMM). Due to the dynamic and complex nature of 5G environments, the HMM is tailored to capture the sequential patterns in delay data, with the Viterbi algorithm employed to predict short-term and medium-term end-to-end delay ranges. We conduct a thorough performance evaluation, assessing the prediction rates using different amounts of prior information and prediction horizons. The results demonstrate that the proposed method might achieve prediction accuracy rates up to 80% and 92%, making it effective for latency-sensitive applications in 5G networks.",
keywords = "End-to-end delay, Estimation, Machine Learning, Neural Networks",
author = "Diyar Fadhil and Rodolfo Oliveira",
note = "info:eu-repo/grantAgreement/FCT/Concurso de Projetos de I&D em Todos os Dom{\'i}nios Cient{\'i}ficos - 2022/2022.08786.PTDC/PT# info:eu-repo/grantAgreement/FCT/Concurso de avalia{\c c}{\~a}o no {\^a}mbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50008%2F2020/PT# Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024 ; Conference date: 25-11-2024 Through 27-11-2024",
year = "2024",
doi = "10.1109/CSCN63874.2024.10849684",
language = "English",
series = "2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "120--123",
booktitle = "2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024",
address = "United States",
}