Deep convolutional neural networks for massive MIMO fingerprint-based positioning

Joao Vieira, Erik Leitinger, Muris Sarajlic, Xuhong Li, Fredrik Tufvesson

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.
Original languageEnglish
Title of host publication2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC
PublisherInstitute of Electrical and Electronics Engineers
Pages1 - 6
Number of pages6
ISBN (Electronic)978-153863531-5
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes
Event28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications: PIMRC 2017 - Montreal, Canada
Duration: 8 Oct 201713 Oct 2017

Conference

Conference28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Country/TerritoryCanada
CityMontreal
Period8/10/1713/10/17

Keywords

  • stat.ML
  • cs.IT
  • math.IT

Fingerprint

Dive into the research topics of 'Deep convolutional neural networks for massive MIMO fingerprint-based positioning'. Together they form a unique fingerprint.

Cite this