# Decimeter Ranging with Channel State Information

arXiv: https://arxiv.org/abs/1902.09652 · HTML: https://mlsanity.com/paper/1902.09652/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

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

- [CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI](https://mlsanity.com/paper/2211.10354/) (2022)
- [DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi](https://mlsanity.com/paper/2507.12132/) (2025)
- [Towards SISO Bistatic Sensing for ISAC](https://mlsanity.com/paper/2508.12614/) (2025)
- [Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing](https://mlsanity.com/paper/2210.06416/) (2022)
- [Reconstructing Depth Images of Moving Objects from Wi-Fi CSI Data](https://mlsanity.com/paper/2503.06458/) (2025)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition](https://mlsanity.com/paper/2509.15129/) (2025)

## Abstract (arXiv)

This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized MIMO-OFDM WLAN receivers. It was often claimed that the CSI phase is contaminated with errors of known and unknown natures rendering ToF-based positioning difficult. To search for an answer, we take a bottom-up approach by first understanding CSI, its constituent building blocks, and the sources of error that contaminate it. We then model these effects mathematically. The correctness of these models is corroborated based on the CSI collected in extensive measurement campaign including radiated, conducted and chamber tests. Knowing the nature of contamination in CSI phase and amplitude, we proceed with introducing pre-processing methods to clean CSI from those errors and make it usable for range estimation. To check the validity of proposed algorithms, the MUSIC super-resolution algorithm is applied to post-processed CSI to perform range estimates. Results substantiate that median accuracy of 0.6m, 0.8m, and 0.9m is achievable in highly multipath line-of-sight environment where transmitter and receiver are 5m, 10m, and 15m apart.