Channel Charting for Streaming CSI Data
- Resource Type
- Conference
- Authors
- Taner, Sueda; Guillaud, Maxime; Tirkkonen, Olav; Studer, Christoph
- Source
- 2023 57th Asilomar Conference on Signals, Systems, and Computers Signals, Systems, and Computers, 2023 57th Asilomar Conference on. :1648-1653 Oct, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Dimensionality reduction
Computers
Computer architecture
Data mining
Task analysis
Channel state information
- Language
- ISSN
- 2576-2303
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 ma intai ns 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.