8 research outputs found
Channel Charting for Streaming CSI Data
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.Comment: Presented at the 2023 Asilomar Conference on Signals, Systems, and
Computer
Velocity-Based Channel Charting with Spatial Distribution Map Matching
Fingerprint-based localization improves the positioning performance in
challenging, non-line-of-sight (NLoS) dominated indoor environments. However,
fingerprinting models require an expensive life-cycle management including
recording and labeling of radio signals for the initial training and regularly
at environmental changes. Alternatively, channel-charting avoids this labeling
effort as it implicitly associates relative coordinates to the recorded radio
signals. Then, with reference real-world coordinates (positions) we can use
such charts for positioning tasks. However, current channel-charting approaches
lag behind fingerprinting in their positioning accuracy and still require
reference samples for localization, regular data recording and labeling to keep
the models up to date. Hence, we propose a novel framework that does not
require reference positions. We only require information from velocity
information, e.g., from pedestrian dead reckoning or odometry to model the
channel charts, and topological map information, e.g., a building floor plan,
to transform the channel charts into real coordinates. We evaluate our approach
on two different real-world datasets using 5G and distributed
single-input/multiple-output system (SIMO) radio systems. Our experiments show
that even with noisy velocity estimates and coarse map information, we achieve
similar position accuraciesComment: This work has been submitted to the IEEE for possible publication.
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Improving Channel Charting with Representation-Constrained Autoencoders
Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns