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Service Time Analysis of Go-Back-N ARQ Protocol

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

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Abstract

Automatic Repeat reQuest (ARQ) protocols are needed to control transmission errors at Data Link Layer (DLL) of OSI (Open Systems Interconnection) network model and to provide smooth and reliable transmission between network nodes. Using acknowledgements and timeouts are essential in different types of ARQ protocols. Types of ARQ protocols include Stop-and-wait (SW) ARQ, Go-Back-N (GBN) ARQ, and Selective Repeat (SR) ARQ. In this paper we continue the performance measurement of Go-Back-N ARQ protocol. A new mathematical model is proposed to analyze and measure the service time of Go-Back-N ARQ protocol over noisy channels. The system is modeled as a stochastic process. The distributions of service time is focused on. Probability Generating Functions (PGF) of service time is derived in terms of message size and error rate parameters. Furthermore, the average and second moment of service time distribution are also calculated. Results of this analysis can be considered in the study and simulation of similar network models. Moreover, they can be used in approximation of similar or relevant communication systems.

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Authors and Affiliations

  1. Department of Information Technology, Faculty of Computers and Information, Suez University, Suez, Egypt

    Fayza A. Nada

Authors
  1. Fayza A. Nada
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Correspondence to Fayza A. Nada .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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Nada, F.A. (2021). Service Time Analysis of Go-Back-N ARQ Protocol. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_53

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