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Localization Performance for eNodeBs using Solitary and Fused RSS-Modeling Approaches
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
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
2019 (English)In: IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The problem of locating radio devices has been addressed by a variety of methods. In the cellular setting, most of the focus have been on locating user equipment (UE). This work focuses on the inverse problem, i.e. locating the eNodeB based on received signal strength (RSS) measurements collected by UEs. We perform a comprehensive evaluation of six variations of two RSS-modeling based localization approaches. Furthermore, two methods for fusing the location estimates of the individual cells were also examined. The evaluation is done using a manually created ground truth data set for eNodeB positions, and a large measurement data set comprising of more than four million observations collected from cellular modems onboard Swedish trains. The best localization accuracy was obtained by one of our proposed variations of logloss fitting using geographic aggregation with highest mean RSRP as the reference point selection criteria. When combined with centroid-based fusion of the individual cell estimates, a median eNodeB localization error of 433 m was obtained, which is a considerable improvement over the second-best approach which achieved a median error of 674 m. The centroid-based fusion approach was found to consistently outperform the DPD fusion approach, which in turn had a better localization error distribution than obtained for solitary cells.

Place, publisher, year, edition, pages
IEEE, 2019.
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-75899DOI: 10.1109/WiMOB.2019.8923521ISBN: 978-1-7281-3315-7 (print)ISBN: 978-1-7281-3316-4 (electronic)OAI: oai:DiVA.org:kau-75899DiVA, id: diva2:1376752
Conference
The 15th International Conference on Wireless and Mobile Computing (WiMob 2019)Barcelona, Spain 21-23 october
Projects
HITS, 4707
Funder
Knowledge FoundationAvailable from: 2019-12-10 Created: 2019-12-10 Last updated: 2019-12-12Bibliographically approved

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