Performance evaluation of LTE radio fingerprinting using field measurements

Turkka, Jussi; Hiltunen, Tuomas; Mondal, Riaz Uddin; Ristaniemi, Tapani

doi:10.1109/iswcs.2015.7454387

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…User position is the localization tag of the best matching fingerprint [10]. Fingerprinting is not explicitly defined in LPP; however, there are some works describing its implementation on the basis of the LTE positioning infrastructure [13,14].…”

Section: Related Workmentioning

confidence: 99%

Modeling of Real Time Kinematics localization error for use in 5G networks

Hoffmann

Kryszkiewicz

Koudouridis

2020

J Wireless Com Network

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In 5G networks information about localization of a user equipment (UE) can be used not only for emergency calls or location-based services, but also for the network optimization applications, e.g., network management or dynamic spectrum access by using Radio Environment Maps (REM). However, some of these applications require much better localization accuracy than currently available in 4G systems. One promising localization method is Global Navigation Satellite System (GNSS)-based Real-Time Kinematics (RTK). While the signal received from satellites is the same as in traditional GNSS, a new reception method utilizing real-time data from a nearby reference station (e.g., 5G base station) results in cm-level positioning accuracy. The aim of this paper is to obtain a model of the RTK localization error for smartphone-grade GNSS antenna under open-sky conditions, that can be used in 5G network simulators. First, a tutorial-style overview of RTK positioning, and satellite orbits prediction is provided. Next, an RTK localization simulator is implemented utilizing GNSS satellites constellations. Results are investigated statistically to provide a simple, yet accurate RTK localization error framework, which is based on two Gauss-Markov process generators parametrized by visible satellites geometry, UE motion, and UE-satellite distance error variance.

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Section: Related Workmentioning

confidence: 99%

Modeling of Real Time Kinematics localization error for use in 5G networks

Hoffmann

Kryszkiewicz

Koudouridis

2020

J Wireless Com Network

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“…Recently, the indoor and outdoor localization technology based on Base Station (BS) signal has been gaining attention again. Benefiting from the rapid development of big data storage and processing technology [1]- [4], as well as 3rd Generation Partnership Project (3GPP) R9's support for user equipment (UE) reporting Minimization of Drive-Tests (MDT) measurement reports [5], then communication service providers (CSPs) are able to collect, store and locating massive MDT data of the whole network, and then utilize these located MDT data to replace traditional drive test (DT) data to improve the efficiency of network optimization and reduce the cost of network operation and maintenance (O&M) [6]. In addition, with the advantage of wide coverage, the whole located MDT data can also be utilized in public security, such as crowd impact analysis for epidemic prevention assistance, suspect tracking, etc.…”

Section: Introduction a Csp Mdt Indoor Localization Scenementioning

confidence: 99%

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

Wei

Xie

2021

IEEE Access

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In this paper, a commercial high-accuracy, low-cost and real-time indoor building-level localization system is proposed, which is applicable for locating the Minimization of Drive-Tests (MDT) data in the long-term-evolution (LTE) cellular communication network system. The system utilizes MDT data containing Global Navigation Satellite Systems (GNSS) information which is easy to collect and low cost to assist indoor localization, instead of using indoor drive test (DT) data which needs high manual collection and maintenance costs. In order to compensate for the loss of location accuracy, this paper innovatively divide the online process into two phases: indoor and outdoor (IO) identification phase and indoor localization phase. A real-time and precise GMM-based unsupervised algorithm is applied to identifying if the non-GNSS MDT data is in indoor environment in IO identification phase. Then, a multi-class classification algorithm based on Bayesian classifier is used to locate indoor MDT data to the specific building. The results of experiments conducted in an in-service LTE network using more than 100 LTE base stations demonstrate that the proposed technique yields a IO identification accuracy of 90% and an indoor location accuracy of 49.3m(@67%) respectively, which provides at least 30.2% enhancement in location accuracy compared to traditional technology without DT fingerprint database. This proposed indoor localization technique is applicable in network optimization and Operation and Maintenance (O&M) to assist communication service providers to reduce their operating expense (OPEX) by locating those MDT data without GNSS information. INDEX TERMS Long-term-evolution, indoor localization, indoor and outdoor identification, Bayesian classifier, Gaussian mixture model, penetration loss.

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“…Recently, the localization method based on Base Station(BS) signal has been gaining attention again. Benefiting from the rapid development of big data storage and processing technology [1]- [4], as well as 3GPP R9's support for User Equipment(UE) reporting MDT measurement reports [5], CSPs(communication service providers) are able to collect the massive MDT data reported by UE in the whole network, and locate these MDT data through localization technology, and then utilize these located MDT data to help them to improve the efficiency of network optimization and reduce the cost of network O&M(Operation and Maintenance) [6]. For instance, in network weak coverage analysis and overlapping coverage analysis field, data collected by traditional manual DT(Drive Test) is only a sample of the whole network, which can not effectively show the actual network quality of the whole network in the interested areas.…”

Section: Introductionmentioning

confidence: 99%

An Accuracy and Real-Time Commercial Localization System in LTE Networks

Wei

2020

IEEE Access

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In this paper, an ARTLoc (accuracy and real-time commercial localization) system is proposed, which is applicable for positioning the Minimization of Drive-Tests(MDT) data in the long-termevolution(LTE) cellular communication network system. This system utilizes a collaborative filtering algorithm to ensure the robustness and accuracy of the fingerprint database when both base station position and GPS MDT data are abnormal in commercial communication service provider(CSP) MDT localization scene. Then, a fast matching KNN algorithm including coarse matching and fine matching is proposed to improve the location efficiency and reduce the location cost under the premise of ensuring high location accuracy. The results of experiments conducted in an in-service LTE network using more than 1000 LTE base station demonstrate that the proposed technique yields a location accuracy of 65.7 meters(@67%) with DT data and 62.1 meters(@67%) with massive GPS MDT data(over 10 million), which provides at least 184.2% and 145.2% enhancement in accuracy respectively compared to the traditional technology. In addition, our system provides a more than 7 times improvement in location efficiency compared to traditional technology. This proposed localization technique is applicable in network optimization and Operation and Maintenance(O&M) to assist CSP to reduce their operating expense(OPEX) by positioning those MDT data without GPS location.

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Performance evaluation of LTE radio fingerprinting using field measurements

Cited by 19 publications

References 10 publications

Modeling of Real Time Kinematics localization error for use in 5G networks

Modeling of Real Time Kinematics localization error for use in 5G networks

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

An Accuracy and Real-Time Commercial Localization System in LTE Networks

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