Position location based on measurement reports in LTE cellular networks

Shakir, Zaenab; Zec, Josko; Kostanic, Ivica

doi:10.1109/wamicon.2018.8363501

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…The drive/walk test has been the classic technique to collect radio signal measurements in mobile network operators [18], [35], [36]. These measurements are used for coverage evaluation, operator benchmarking, and trouble shooting and optimizing cellular networks.…”

Section: Methods 21 Datasetmentioning

confidence: 99%

User equipment geolocation depended on long-term evolution signal-level measurements and timing advance

Shakir

Zec

Kostanic

et al. 2023

IJECE

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<p>A new approach is described for investigating the accuracy of positioning active long-term evolution (LTE) users. The explored approach is a network-based method and depends on signal level measurements as well as the coverage of the serving cell. In a two-dimensional coordinate system, the algorithm simultaneously applies LTE measured data with a combination of a basic prediction model to locate the mobile device’s user. Furthermore, we introduce a unique method that combines timing advance (TA) and the measured signal level to narrow the search region and improve accuracy. The developed method is assessed by comparing the predicted results from the proposed algorithm with satellite measurements from the global positioning system (GPS) in various scenarios calculated via the number of cells that user equipment concurrently reports. This work separates seven different cases starting from a single reported cell to five reported cells from up to 3 sites. For analysis, the root mean square error (RMSE) is computed to obtain the validation for the proposed approach. The study case demonstrates location accuracy based on the numbers of registered cells with the mean RMSE improved using TA to approximately 70-191 m for the range of scenarios.</p>

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Section: Methods 21 Datasetmentioning

confidence: 99%

User equipment geolocation depended on long-term evolution signal-level measurements and timing advance

Shakir

Zec

Kostanic

et al. 2023

IJECE

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“…The sensor nodes providing prior location information are known as anchor or reference nodes whose location is determined by the global positioning system (GPS). Recently, researchers have started studying localization in IoT networks for numerous applications [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. However, all the sensor nodes cannot be equipped with GPS due to the cost, energy efficiency and…”

Section: Introductionmentioning

confidence: 99%

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

2021

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With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

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