Drive Test Minimization Using Deep Learning with Bayesian Approximation

Jakob Thrane, Matteo Artuso, Darko Zibar, Henrik Lehrmann Christiansen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Drive testing is a common practice performed by operators to optimize and evaluate their mobile networks with respect to capacity and coverage. For dense areas, drive test measurements are very time-consuming due to many obstacles causing Non-Line-Of-Sight (NLoS) scenarios. In this paper, we show how Deep Learning (DL) techniques can be utilized to predict LTE signal quality metrics using drive test measurements. Moreover, we show how the obtained solution can offer insight into where additional measurements are required. The proposed solution can accurately predict LTE signal quality metrics reducing drive tests needed by up to 70%.
Original languageEnglish
Title of host publicationProceedings of 2018 IEEE 88th Vehicular Technology Conference
Number of pages5
PublisherIEEE
Publication date2018
Pages1-5
ISBN (Print)9781538663585
DOIs
Publication statusPublished - 2018
Event2018 IEEE 88th Vehicular Technology Conference: VTC2018-Fall - Hilton Chicago, Chicago, United States
Duration: 27 Aug 201830 Aug 2018
http://www.ieeevtc.org/vtc2018fall/

Conference

Conference2018 IEEE 88th Vehicular Technology Conference: VTC2018-Fall
LocationHilton Chicago
CountryUnited States
CityChicago
Period27/08/201830/08/2018
Internet address

Keywords

  • Communication channels
  • Channel models
  • Machine Learning
  • 4G mobile communication

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