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Wireless Networks
Erschienen in: Wireless Networks 8/2019

16.06.2018

An RSS-based regression model for user equipment location in cellular networks using machine learning

verfasst von: L. L. Oliveira, L. A. Oliveira Jr., G. W. A. Silva, R. D. A. Timoteo, D. C. Cunha

Erschienen in: Wireless Networks | Ausgabe 8/2019

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Abstract

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods.
Vorheriger Artikel Iterative hybrid precoder and combiner design for mmWave MIMO-OFDM systems
Nächster Artikel Performance analysis of clustered device-to-device networks using matern cluster process

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Metadaten
Titel
An RSS-based regression model for user equipment location in cellular networks using machine learning
verfasst von
L. L. Oliveira
L. A. Oliveira Jr.
G. W. A. Silva
R. D. A. Timoteo
D. C. Cunha
Publikationsdatum
16.06.2018
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 8/2019
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-018-1774-4

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