Convolutional Neural Networks based Denoising for Indoor Localization

Wafa Njima, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

Original languageEnglish (US)
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189642
StatePublished - Apr 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: Apr 25 2021Apr 28 2021

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online


  • Convolutional Neural Networks (CNN)
  • Indoor localization
  • Matrix completion
  • Received Signal Strength Indicator (RSSI)
  • Trilateration

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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