Correlation-Based Feature Mapping of Crowdsourced LTE Data

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Abstract

There have been efforts taken by different research projects to understand the complexity and the performance of a mobile broadband network. Various mobile network measurement platforms are proposed to collect performance metrics for analysis. Data integration would provide more thorough data analyses as well as prediction and decision models from one dataset to another. The crucial part of the data integration is to find out, whether two datasets have corresponding features (performance metrics). However, finding common features across datasets is a challenging task. For example, features might: 1) have similar names but be different metrics, 2) have different names but be similar metrics, or 3) be same metrics but have differences in the underlying methodology.

We designed a feature mapping methodology between two crowdsourced LTE measurement-based datasets. Our method is based on correlations between the features and the mapping algorithm is solving a maximum constraint satisfaction problem (CSP). We define our constraints as inequality patterns between the correlation coefficients of the measured features. Our results show that the method maps measurement features based on their correlation coefficients with high confidence scores (between 0.78 to 1.0 depending on the amount of features). We observe that mapping score increases as a function of the amount of features. Altogether, our results show that this methodology can be used as an automated tool in the measurement data integration.

Details

Original languageEnglish
Title of host publicationIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications Workshops
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on Personal, Indoor and Mobile Radio Communications - Polo Congressuale, Bologna, Italy
Duration: 9 Sep 201812 Sep 2018
Conference number: 29
http://pimrc2018.ieee-pimrc.org/

Publication series

NameIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
ISSN (Print)2166-9570
ISSN (Electronic)2166-9589

Conference

ConferenceIEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Abbreviated titleIEEE PIMRC
CountryItaly
CityBologna
Period09/09/201812/09/2018
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