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Characterizing Packet Losses in Vehicular Networks
Department of Information Systems, St. Petersburg University of Information Technologies, Mechanics and Optics, St.-Petersburg, Russia & Institute of Computer Science, University of Tartu, Estonia.ORCID iD: 0000-0002-6460-4626
Department of Information Systems, St. Petersburg University of Information Technologies, Mechanics and Optics, St.-Petersburg, Russia & Institute of Computer Science, University of Tartu, Estonia.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-1155-5717
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).ORCID iD: 0000-0003-1460-2988
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2019 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 68, no 9, p. 8347-8358Article in journal (Refereed) Published
Abstract [en]

To enable testing and performance evaluation of new connected and autonomous driving functions, it is important to characterize packet losses caused by degradation in vehicular (V2X) communication channels. In this paper we suggest an approach to constructing packet loss models based on the socalled Pseudo-Markov chains (PMC). The PMC based model needs only short training sequences, has low computational complexity, and yet provides more precise approximations than known techniques. We show how to learn PMC models from either empirical records of packet receptions, or from analytical models of fluctuations in the received signal strength. In particular, we validate our approach by applying it on (i) V2X packet reception data collected from an active safety test run, which used the LTE network of the AstaZero automotive testing site in Sweden, and (ii) variants of the Rician fading channel models corresponding to two models of correlations of packet losses. We also show that initializing the Baum-Welch algorithm with a second order PMC model leads to a high accuracy model. © 2019 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2019. Vol. 68, no 9, p. 8347-8358
Keywords [en]
Autonomous vehicles, cooperative ITS, VANET, V2X, channel estimation, hidden Markov models, fading channels
National Category
Communication Systems Telecommunications
Identifiers
URN: urn:nbn:se:hh:diva-40265DOI: 10.1109/TVT.2019.2930689ISI: 000487191500006Scopus ID: 2-s2.0-85073793712OAI: oai:DiVA.org:hh-40265DiVA, id: diva2:1338892
Projects
AstaMoCA ”Model-based Communication Architecture for the AstaZero Automotive Safety” project (2017–2019)
Funder
Knowledge FoundationSwedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

A short version of this paper has been presented at 2019 IEEE International Symposium on Information Theory (ISIT) which was held on July 7-12, 2019 in Paris, France.

Funding: the Knowledge Foundation in the framework of AstaMoCA ”Model-based Communication Architecture for the AstaZero Automotive Safety” project (2017–2019), COST Action CA15127 (”Resilient communication services protecting end-user applications from disaster-based failures – RECODIS”) supported by COST (European Cooperation in Science and Technology), Swedish Foundation for Strategic Research (SSF) in the framework of Strategic Mobility Program (2019-2020), from the Estonian Research Council under the grant PRG49 and from the ELLIIT Strategic Research Network.

Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2020-02-03Bibliographically approved

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Rabi, MabenLyamin, NikitaVinel, Alexey

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