# Deep Transfer Learning for WiFi Localization

arXiv: https://arxiv.org/abs/2103.05123 · HTML: https://mlsanity.com/paper/2103.05123/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization](https://mlsanity.com/paper/2004.04618/) (2020)
- [Wireless Localisation in WiFi using Novel Deep Architectures](https://mlsanity.com/paper/2010.08658/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization](https://mlsanity.com/paper/2504.08811/) (2025)
- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)
- [TablEye: Seeing small Tables through the Lens of Images](https://mlsanity.com/paper/2307.02491/) (2023)
- [Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation](https://mlsanity.com/paper/2203.04032/) (2022)
- [EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning](https://mlsanity.com/paper/2205.10356/) (2022)
- [MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2605.01369/) (2026)

## Abstract (arXiv)

This paper studies a WiFi indoor localisation technique based on using a deep learning model and its transfer strategies. We take CSI packets collected via the WiFi standard channel sounding as the training dataset and verify the CNN model on the subsets collected in three experimental environments. We achieve a localisation accuracy of 46.55 cm in an ideal (6.5m × 2.5m) office with no obstacles, 58.30 cm in an office with obstacles, and 102.8 cm in a sports hall (40 × 35m). Then, we evaluate the transfer ability of the proposed model to different environments. The experimental results show that, for a trained localisation model, feature extraction layers can be directly transferred to other models and only the fully connected layers need to be retrained to achieve the same baseline accuracy with non-transferred base models. This can save 60% of the training parameters and reduce the training time by more than half. Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55% of the training data is required to obtain the models' accuracy similar to the base models.