A survey of deep learning approaches for WiFi-based indoor positioning

Xu Feng, Khuong An Nguyen, Zhiyuan Luo

Research output: Contribution to journalArticlepeer-review


One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.
Original languageEnglish
Pages (from-to)163-216
Number of pages54
JournalJournal of Information and Telecommunication
Issue number2
Publication statusPublished - 20 Sept 2021

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited


  • deep learning
  • neural network
  • WiFi fingerprinting
  • Deep learning


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