Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer

@inproceedings{Rappaport2021ImprovingCC,
  title={Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer},
  author={Brian Rappaport and Emre Gonultacs and Jakob Hoydis and Maximilian Arnold and Pavan Koteshwar Srinath and Christoph Studer},
  year={2021}
}
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points. In this paper, we introduce a new dimensionality reduction method specifically designed for channel charting using a novel split triplet loss, which utilizes physical information available during the CSI acquisition… 
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TLDR
This paper demonstrates that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process and shows 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.
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