Network-side Localization via Semi-Supervised Multi-point Channel Charting

Junquan Deng, Olav Tirkkonen, Jianzhao Zhang, Xianlong Jiao, Christoph Studer

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

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Abstract

We consider the network-side mobile localization problem in future 5G and beyond wireless networks with distributed multi-antenna base stations (BSs). For this application, we propose a semi-supervised multi-point channel charting (SS-MPCC) framework, which consists of (i) collaborative collection of channel state information (CSI) and other side-information by distributed BSs; (ii) local CSI feature extraction and self-learning of a dissimilarity metric, and (iii) global graph construction and constrained manifold learning. We show that side-information from routine network operations, including timestamps, channel qualities, and a small set of labeled samples, can be exploited to construct a consistent global graph. The graph is then mapped to a 2D channel chart using constrained manifold learning for localization purposes. We evaluate the performance of SS-MPCC in a simulated urban outdoor scenario with realistic user motion. Our results show that SS-MPCC achieves a mean localization error of 5.6 m with only 10% of labeled CSI samples. SS-MPCC does not require accurate synchronization among multiple BSs and is promising for future cellular localization.
Original languageEnglish
Title of host publicationProceedings of International Wireless Communications and Mobile Computing, IWCMC 2021
PublisherIEEE
Pages1654-1660
Number of pages7
ISBN (Electronic)978-1-7281-8616-0
ISBN (Print)978-1-7281-8617-7
DOIs
Publication statusPublished - 9 Aug 2021
MoE publication typeA4 Article in a conference publication
EventInternational Wireless Communications and Mobile Computing Conference - Harbin, China
Duration: 28 Jun 20212 Jul 2021

Conference

ConferenceInternational Wireless Communications and Mobile Computing Conference
Abbreviated titleIWCMC
Country/TerritoryChina
CityHarbin
Period28/06/202102/07/2021

Keywords

  • Location awareness
  • Measurement
  • Manifolds
  • Laplace equations
  • Wireless networks
  • Real-time systems
  • Manifold learning

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