# Efficient channel charting via phase-insensitive distance computation

arXiv: https://arxiv.org/abs/2104.13184 · HTML: https://mlsanity.com/paper/2104.13184/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey](https://mlsanity.com/paper/2009.08136/) (2020)
- [Improving Channel Charting with Representation-Constrained Autoencoders](https://mlsanity.com/paper/1908.02878/) (2019)
- [CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion](https://mlsanity.com/paper/2009.02798/) (2020)
- [Similarity-based prediction for channel mapping and user positioning](https://mlsanity.com/paper/2101.05217/) (2020)
- [Parametric channel estimation for massive MIMO](https://mlsanity.com/paper/1710.08214/) (2017)

## Built on by (most cited first, 8 of 8)

- [Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting](https://mlsanity.com/paper/2204.13996/) (2022)
- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [Channel charting based beamforming](https://mlsanity.com/paper/2212.12340/) (2022)
- [Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting](https://mlsanity.com/paper/2310.03762/) (2023)
- [Velocity-Based Channel Charting with Spatial Distribution Map Matching](https://mlsanity.com/paper/2311.08016/) (2023)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [CSI Compression using Channel Charting](https://mlsanity.com/paper/2501.01431/) (2024)
- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)

## Abstract (arXiv)

Channel charting is an unsupervised learning task whose objective is to encode channels so that the obtained representation reflects the relative spatial locations of the corresponding users. It has many potential applications, ranging from user scheduling to proactive handover. In this paper, a channel charting method is proposed, based on a distance measure specifically designed to reduce the effect of small scale fading, which is an irrelevant phenomenon with respect to the channel charting task. A nonlinear dimensionality reduction technique aimed at preserving local distances (Isomap) is then applied to actually get the channel representation. The approach is empirically validated on realistic synthetic newmultipath MIMO channels, achieving better results than previously proposed approaches, at a lower cost.