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… 
View PDF on arXiv

Figures and Tables from this paper

References

SHOWING 1-10 OF 21 REFERENCES
Improving Channel Charting with Representation -Constrained Autoencoders
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.
Triplet-Based Wireless Channel Charting
TLDR
This work focuses on neural network-based approaches, and proposes a new training strategy based on triplets of samples that allows to simultaneously learn a meaningful similarity metric between CSI samples, on the basis of proximity in their respective acquisition times, and to perform the sought dimensionality reduction.
Siamese Neural Networks for Wireless Positioning and Channel Charting
TLDR
A unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting and enables semisupervised positioning, where only a small set of location information is available during training.
Channel Charting: Locating Users Within the Radio Environment Using Channel State Information
TLDR
Channel charting (CC) is proposed, a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area that enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, hand-over, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base station.
An Out-of-Sample Extension for Wireless Multipoint Channel Charting
TLDR
Extensions of MPCC to out-of-sample UE CSIs are investigated in this paper, when Laplacian-Eigenmaps (LE) is used for dimensional reduction.
Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning
TLDR
A novel channel sounder architecture, capable of measuring multiantenna and multi-subcarrier channel state information (CSI) at different frequency bands, antenna geometries and propagation environments is introduced, verified by evaluation of channel data from first measurements.
FaceNet: A unified embedding for face recognition and clustering
TLDR
A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
TLDR
This paper proposes a positioning pipeline for wireless LAN MIMO-OFDM systems which uses uplink CSI measurements obtained from one or more unsynchronized access points (APs) to achieve centimeter-level median distance error, an order of magnitude improvement over a conventional baseline.
An Indoor Passive Positioning Method Using CSI Fingerprint Based on Adaboost
TLDR
The Adaboost positioning system (ABPS) is proposed that uses the phase information in CSI and the ensemble learning (EL) method to train the fingerprint map and can achieve position coordinates regression by means of confidence level.
QuaDRiGa: A 3-D Multi-Cell Channel Model With Time Evolution for Enabling Virtual Field Trials
TLDR
This paper extends the popular Wireless World Initiative for New Radio (WINNER) channel model with new features to make it as realistic as possible and can accurately predict the performance for an urban macro-cell setup with commercial high-gain antennas.
...
1
2
3
...