mlsanityData to 14 Oct 2026

Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

arXiv 2310.03762 ↗First posted 28 Sep 2023eess.SP cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

20 of 45 references.

References · 20

  1. 2023Sionna RT: Differentiable Ray Tracing for Radio Propagation Modelingcited by 62
  2. 2018Channel Charting: Locating Users within the Radio Environment using Channel State Informationcited by 40
  3. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  4. 2020Triplet-Based Wireless Channel Charting: Architecture and Experimentscited by 17
  5. 2017Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioningcited by 16
  6. 20206G White Paper on Machine Learning in Wireless Communication Networkscited by 14
  7. 2023Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Chartingcited by 11
  8. 2019Improving Channel Charting with Representation-Constrained Autoencoderscited by 10
  9. 2019Siamese Neural Networks for Wireless Positioning and Channel Chartingcited by 10
  10. 2020DNN-based Localization from Channel Estimates: Feature Design and Experimental Resultscited by 10
  11. 2022Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel chartingcited by 10
  12. 2023Wireless Channel Charting: Theory, Practice, and Applicationscited by 9
8 more
  1. 2021Efficient channel charting via phase-insensitive distance computationcited by 8
  2. 2022Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approachcited by 8
  3. 2018CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approachcited by 6
  4. 2021Integration of Communication and Sensing in 6G: a Joint Industrial and Academic Perspectivecited by 5
  5. 2021Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizercited by 5
  6. 2022Channel charting based beamformingcited by 5
  7. 2021Positioning in 5G networkscited by 3
  8. 2022Model-Driven End-to-End Learning for Integrated Sensing and Communicationcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 2.

5 papers.

  1. 2023Velocity-Based Channel Charting with Spatial Distribution Map Matchingcited by 5
  2. 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
  3. 2023Model-based Deep Learning for Beam Prediction based on a Channel Chartcited by 4
  4. 2024Model-based learning for multi-antenna multi-frequency location-to-channel mappingcited by 3
  5. 2025Model-based Implicit Neural Representation for sub-wavelength Radio Localizationcited by 0