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LTE for Trains - Performance Interactions Examined with DL, ML and Resampling
Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO, Computer Networking)ORCID iD: 0000-0003-3461-7079
(DISCO, Computer Networking)
Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO, Computer Networking)ORCID iD: 0000-0001-7311-9334
2019 (English)In: 2019 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2019Conference paper (Refereed)
Abstract [en]

Current LTE networks provide a large fraction of the mobile communication needs. One recent application area that have attained additional interest is the provision of mobile communication services to train passengers. To allow more efficient use of network resources and better onboard communication experience, onboard traffic aggregation can be performed. In this work we examine a large-scale operational data set from a routerbased LTE traffic aggregation system mounted onboard more than 100 trains belonging to a major Swedish train operator. We use both deep learning (DL) with Deep Neural Networks and traditional machine learning (ML) with Random Forests to examine an observed association between train velocity and achieved throughput, which curiously varies over different radio conditions. More than 37000 train journeys are analyzed to explore for structure and learn potential explanatory features. The results indicate that the association has a limited presence on a per cell basis, and that there is only a limited amount of learnable structure per cell. A resampling evaluation shows that the association becomes apparent when cell measurements are aggregated at an order of tens to a hundred cells.

Place, publisher, year, edition, pages
IEEE, 2019.
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-76644DOI: 10.1109/ISCC47284.2019.8969727ISBN: 978-1-7281-2999-0 (electronic)ISBN: 978-1-7281-3000-2 (print)OAI: oai:DiVA.org:kau-76644DiVA, id: diva2:1390912
Conference
IEEE Symposium on Computers and Communications (ISCC) 29 june-3 july 2019, Barcelona, Spain
Projects
HITS, 4707
Funder
Knowledge FoundationAvailable from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-03

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Garcia, JohanBrunstrom, Anna

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