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Channel charting is an emerging technology that enables self-supervisedpseudo-localization of user equipments by performing dimensionality reductionon large channel-state information (CSI) databases that are passively collectedat infrastructure base stations or access points. In this paper, we introduce anew dimensionality reduction method specifically designed for channel chartingusing a novel split triplet loss, which utilizes physical information availableduring the CSI acquisition process. In addition, we propose a novel regularizerthat exploits the physical concept of inertia, which significantly improves thequality of the learned channel charts. We provide an experimental verificationof our methods using synthetic and real-world measured CSI datasets, and wedemonstrate that our methods are able to outperform the state-of-the-art inchannel charting based on the triplet loss.

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