978-951-39-7285-1_vaitos13122017.pdf

Radio frequency fingerprinting for outdoor user equipment localization

Abstract
The recent advancements in cellular mobile technology and smart phone usage have opened opportunities for researchers and commercial companies to develop ubiquitous low cost localization systems. Radio frequency (RF) fingerprinting is a popular positioning technique which uses radio signal strength (RSS) values from already existing infrastructures to provide satisfactory user positioning accuracy in indoor and densely built outdoor urban areas where Global Navigation Satellite System (GNSS) signal is poor and hard to reach. However a major requirement for the RF fingerprinting to maintain good localization accuracy is the collection and updating of large training database. The Minimization of Drive Tests (MDT) functionality proposed by 3GPP LTE Release 10 & 11 has enabled cellular operators to autonomously gather and update necessary amount of RF fingerprint samples by utilizing their subscriber user equipments (UEs). The main objective of this thesis is to propose a framework for RF fingerprint positioning (RFFP) of outdoor UEs using MDT data and to further improve its performance capability to provide better localization. In the first part only LTE base-station (BS) RSS values were used to improve grid-based RF fingerprint positioning (G-RFFP) by using novel approaches: using overlapped grid-cell layouts (GCL), weighting based grid-cell unit selection and Artificial Intelligence based G-RFFP method. In the second part real measurement RSS values from LTE BS and WLAN access points (APs) were utilized and a generic measurement method referred to as GMDT was proposed to correlate WLAN RSS to LTE RSS measurements and its significance to RFFP was studied using a partial fingerprint matching technique. To remove the computational cost associated with training data preprocessing a new cluster-based RF fingerprint positioning (C-RFFP) method was proposed. This thesis provides a good source of information and novel techniques for cellular operators to build a low cost RF fingerprint positioning system which can deliver acceptable results in emergency user localization
Main Author
Mondal, Riaz Uddin
Format
Theses Doctoral thesis
Published
2017
Series
Jyväskylä studies in computing
Subjects
RF fingerprinting
LTE
WLAN
Mahalanobis distance
Kullback-Leibler divergence
K-Nearest Neighbor
K-means clustering
hierarchical clustering
Fuzzy C-means Clustering
paikannus
mobiililaitteet
radioaallot
matkaviestinverkot
langattomat lähiverkot
koneoppiminen
klusterianalyysi
ISBN
978-951-39-7285-1
Publisher
University of Jyväskylä
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-7285-1Use this for linking
ISSN
1456-5390
Language
English
Published in
Jyväskylä studies in computing
License
In CopyrightOpen Access

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description The recent advancements in cellular mobile technology and smart phone usage have opened opportunities for researchers and commercial companies to develop ubiquitous low cost localization systems. Radio frequency (RF) fingerprinting is a popular positioning technique which uses radio signal strength (RSS) values from already existing infrastructures to provide satisfactory user positioning accuracy in indoor and densely built outdoor urban areas where Global Navigation Satellite System (GNSS) signal is poor and hard to reach. However a major requirement for the RF fingerprinting to maintain good localization accuracy is the collection and updating of large training database. The Minimization of Drive Tests (MDT) functionality proposed by 3GPP LTE Release 10 & 11 has enabled cellular operators to autonomously gather and update necessary amount of RF fingerprint samples by utilizing their subscriber user equipments (UEs). The main objective of this thesis is to propose a framework for RF fingerprint positioning (RFFP) of outdoor UEs using MDT data and to further improve its performance capability to provide better localization. In the first part only LTE base-station (BS) RSS values were used to improve grid-based RF fingerprint positioning (G-RFFP) by using novel approaches: using overlapped grid-cell layouts (GCL), weighting based grid-cell unit selection and Artificial Intelligence based G-RFFP method. In the second part real measurement RSS values from LTE BS and WLAN access points (APs) were utilized and a generic measurement method referred to as GMDT was proposed to correlate WLAN RSS to LTE RSS measurements and its significance to RFFP was studied using a partial fingerprint matching technique. To remove the computational cost associated with training data preprocessing a new cluster-based RF fingerprint positioning (C-RFFP) method was proposed. This thesis provides a good source of information and novel techniques for cellular operators to build a low cost RF fingerprint positioning system which can deliver acceptable results in emergency user localization
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spellingShingle Radio frequency fingerprinting for outdoor user equipment localization Mondal, Riaz Uddin RF fingerprinting LTE WLAN Mahalanobis distance Kullback-Leibler divergence K-Nearest Neighbor K-means clustering hierarchical clustering Fuzzy C-means Clustering paikannus mobiililaitteet radioaallot matkaviestinverkot langattomat lähiverkot koneoppiminen klusterianalyysi Jyväskylä studies in computing
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title Radio frequency fingerprinting for outdoor user equipment localization
title_full Radio frequency fingerprinting for outdoor user equipment localization
title_fullStr Radio frequency fingerprinting for outdoor user equipment localization
title_full_unstemmed Radio frequency fingerprinting for outdoor user equipment localization
title_short Radio frequency fingerprinting for outdoor user equipment localization
title_sort Radio frequency fingerprinting for outdoor user equipment localization
topic RF fingerprinting LTE WLAN Mahalanobis distance Kullback-Leibler divergence K-Nearest Neighbor K-means clustering hierarchical clustering Fuzzy C-means Clustering paikannus mobiililaitteet radioaallot matkaviestinverkot langattomat lähiverkot koneoppiminen klusterianalyysi
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