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  • 1.
    Garcia, Johan
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Sundberg, Simon
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Brunström, Anna
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Beckman, Claes
    Icomera AB, Gothenburg, Sweden.
    Interactions Between Train Velocity andCellular Link Throughput - An Extensive Study2019In: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019Conference paper (Refereed)
    Abstract [en]

    Providing reliable internet connectivity to train passengers can be handled with onboard aggregation routers that use multiple external antennas to simultaneously convey user traffic over multiple links. This work studies the operational characteristics of a large-scale deployment of such a system. The examination focuses on how train velocity is associated with achieved link throughput, and how various interaction effects influence the relationship. A large data set collected over three years is analyzed, indicating that there is a systematic association between train velocity and link throughput that varies over the radio conditions and which is also linked to differences between operators.

  • 2.
    Sundberg, Simon
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Garcia, Johan
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Localization Performance for eNodeBs using Solitary and Fused RSS-Modeling Approaches2019In: IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications, IEEE, 2019Conference 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.

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