# CSI-Based Localization with CNNs Exploiting Phase Information

arXiv: https://arxiv.org/abs/2101.08983 · HTML: https://mlsanity.com/paper/2101.08983/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2021.

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

- [Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning](https://mlsanity.com/paper/2210.14510/) (2022)
- [Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned](https://mlsanity.com/paper/2306.17062/) (2023)
- [Towards Sustainable Deep Learning for Wireless Fingerprinting Localization](https://mlsanity.com/paper/2201.09071/) (2022)
- [Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning](https://mlsanity.com/paper/2410.19788/) (2024)
- [Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning](https://mlsanity.com/paper/2410.20680/) (2024)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning](https://mlsanity.com/paper/2405.11816/) (2024)

## Abstract (arXiv)

In this paper we study the use of the Channel State Information (CSI) as fingerprint inputs of a Convolutional Neural Network (CNN) for localization. We examine whether the CSI can be used as a distinct fingerprint corresponding to a single position by considering the inconsistencies with its raw phase that cause the CSI to be unreliable. We propose two methods to produce reliable fingerprints including the phase information. Furthermore, we examine the structure of the CNN and more specifically the impact of pooling on the positioning performance, and show that pooling over the subcarriers can be more beneficial than over the antennas.