Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

Peral-Rosado, José A. del; Raulefs, Ronald; López-Salcedo, José A.; Seco-Granados, Gonzalo

doi:10.1109/comst.2017.2785181

Cited by 437 publications

(130 citation statements)

References 101 publications

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“…Most of the cellular networks only provide basic localization methods and assistance data for GNSS, essentially because of the increased costs network operators should otherwise face. 3GPP LTE (4.5G) includes potential enhancements for positioning technologies which are both depending on Radio Access Technology (RAT), and RAT-independent, as summarized below [23]: In Release 13 of TS 36.305 [24], the accuracy requirements for ranging or Reference Signal Time Difference (RSTD) measurements were updated, according to the use of received signals at the same carrier frequency (intra-frequency measurements) or at different carrier frequencies (inter-frequency measurements), as shown in Table II of [23]. As discussed in [25], 5G networks aim for sub-meter accuracy in 95% of served areas, comprising both indoor and urban environments, thus overcoming by at least one order of magnitude the accuracy level of the current state-of-the-art solutions, including GNSS in non-Line Of Sight (LOS).…”

Section: Measurement Techniquesmentioning

confidence: 99%

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Spinsante

Stallo

2020

Sensors

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In recent years, the development of advanced systems and applications has propelled the adoption of autonomous railway traffic and train positioning, with several ongoing initiatives and experimental testbeds aimed at proving the suitability and reliability of the Global Navigation Satellite System signals and services, in this specific application domain. To satisfy the strict safety and accuracy requirements aimed at assuring the position solution’s integrity, availability, accuracy and reliability, recent proposals suggest the hybridization of the Global Navigation Satellite System with other technologies. The integration with localization techniques that are expected to be available with the upcoming fifth generation mobile communication networks is among the most promising approaches. In this work, different approaches to the design of hybrid positioning solutions for the railway sector are examined, under the perspective of the uncertainty evaluation of the attained results and performance. In fact, the way the uncertainty associated to the positioning measurements performed by different studies is reported is often not consistent with the Guide to the Expression of Uncertainty in Measurement, and this makes it very difficult to fairly compare the different approaches in order to identify the best emerging solution. Under this perspective, the review provided by this work highlights a number of open issues that should drive future research activities in this field.

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Section: Measurement Techniquesmentioning

confidence: 99%

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Spinsante

Stallo

2020

Sensors

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“…The development of localization methods in cellular networks started with the formulation of the enhanced 911 (E911) location requirements by the Federal Communications Commission (FCC) of the USA in the 1990s [1]. The aim of the E911 requirements were to locate user equipment (UE) emergency calls with the root-mean square error (RMSE) of 125 m in 67% of all cases [1]. In cellular networks from 2G to 4G, firstly standardization effort was put into fulfil government requirements.…”

Section: Introductionmentioning

confidence: 99%

“…With the networks development UE localization information began to be attractive for operators from a commercial point of view, resulting in introduction of location-based services (e.g. social networking, advertising) [1].…”

Section: Introductionmentioning

confidence: 99%

“…5G networks come with a set of new use cases where UE localization information is necessary, not only for emergency and user-plane applications, but also for Intelligent Transportation Systems Aerial Vehicles or Industrial Applications [2]. Moreover in 5G systems localization, *Correspondence: pawel.kryszkiewicz@put.poznan.pl 1 Chair of Wireless Communications, Poznan University of Technology, Polanka 3, 60-965 Poznan, Poland Full list of author information is available at the end of the article data will be utilized for network optimization applications such as self-organizing networks (SON), network management, or dynamic spectrum access (DSA) [1].…”

Section: Introductionmentioning

confidence: 99%

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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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“…The overall accuracy of Wi-Fi based fingerprint positioning is from few meters to around hundred meters [6,7]. However, comparing the positioning accuracy of the different methods by using the published results of different studies might not be fair, since researchers are using different evaluation environments, methodologies and test-beds.…”

Section: Introductionmentioning

confidence: 99%

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

2019

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Radio Frequency (RF) fingerprinting is widely applied for indoor positioning due to the existing Wi-Fi infrastructure present in most indoor spaces (home, work, leisure, among others) and the widespread usage of smartphones everywhere. It corresponds to a simple idea, the signal signature in a location tends to be stable over the time. Therefore, with the signals received from multiple APs, a unique fingerprint can be created. However, the Wi-Fi signal is affected by many factors which degrade the positioning error range to around a few meters. This paper introduces a collaborative method based on device-to-device (D2D) communication to improve the positioning accuracy using only fingerprinting and the direct communication to nearby devices. The results presented in this paper show that the positioning error can be reduced around 44% by considering D2D communication in the operational stage without adding new infrastructure for fingerprinting or complex resource-consuming filters. Moreover, the presence of very large errors is significantly reduced when the collaborative positioning based on D2D is available.

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Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

Cited by 437 publications

References 101 publications

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

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

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

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