Experimental Performance of Blind Position Estimation Using Deep Learning

Ivo Bizon, Zhongju Li, Ahmad Nimr, Marwa Chafii, Gerhard P. Fettweis

Research output: Contribution to journalConference articlepeer-review


Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.

Original languageEnglish (US)
Pages (from-to)4553-4557
Number of pages5
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: Dec 4 2022Dec 8 2022


  • Blind localization
  • deep learning
  • positioning
  • received signal strength
  • wireless sensor network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


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