University of Jyväskylä

Dissertation: 13.12.2017 MSc Riaz Uddin Mondal (Faculty of Information Technology, Mathematical Information Technology)

Start date: Dec 13, 2017 12:00 PM

End date: Dec 13, 2017 03:00 PM

Location: Mattilanniemi, Agora Gamma

MSc Riaz Uddin Mondal defends his doctoral dissertation in Mathematical Information Technology "Radio Frequency Fingerprinting for Outdoor User Equipment Localization". Opponent professor Mohammed Elmusrati (University of Vaasa) and custos professor Tapani Ristaniemi (University of Jyväskylä). The doctoral dissertation is held in English.

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Riaz Uddin Mondal
MSc Riaz Uddin Mondal defends his doctoral dissertation in Mathematical Information Technology "Radio Frequency Fingerprinting for Outdoor User Equipment Localization". Opponent Professor Mohammed Elmusrati (University of Vaasa) and custos Professor Tapani Ristaniemi (University of Jyväskylä). The doctoral dissertation is held in English.

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.


More information

MSc Riaz Uddin Mondal
riaz.u.mondal@student.jyu.fi
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