# Channel Charting for Streaming CSI Data

arXiv: https://arxiv.org/abs/2312.04514 · HTML: https://mlsanity.com/paper/2312.04514/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [CSI Compression using Channel Charting](https://mlsanity.com/paper/2501.01431/) (2024)
- [Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2511.00919/) (2025)

## Abstract (arXiv)

Channel charting (CC) applies dimensionality reduction to channel state information (CSI) data at the infrastructure basestation side with the goal of extracting pseudo-position information for each user. The self-supervised nature of CC enables predictive tasks that depend on user position without requiring any ground-truth position information. In this work, we focus on the practically relevant streaming CSI data scenario, in which CSI is constantly estimated. To deal with storage limitations, we develop a novel streaming CC architecture that maintains a small core CSI dataset from which the channel charts are learned. Curation of the core CSI dataset is achieved using a min-max-similarity criterion. Numerical validation with measured CSI data demonstrates that our method approaches the accuracy obtained from the complete CSI dataset while using only a fraction of CSI storage and avoiding catastrophic forgetting of old CSI data.