mlsanityData to 7 Oct 2026

Channel charting based beamforming

arXiv 2212.12340 ↗First posted 6 Dec 2022cs.NI cs.LG eess.SPcited by 5

Not on the map. Why?

Abstract (arXiv)

Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

17 of 25 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017An Introduction to Deep Learning for the Physical Layercited by 227
  4. 2019DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applicationscited by 121
  5. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  6. 2018Learning to Detectcited by 60
  7. 2018Channel Charting: Locating Users within the Radio Environment using Channel State Informationcited by 40
  8. 2018Model-free Training of End-to-end Communication Systemscited by 29
  9. 2019Massive MIMO Channel Estimation with an Untrained Deep Neural Networkcited by 21
  10. 2019Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequencycited by 16
  11. 2020Triplet-Based Wireless Channel Charting: Architecture and Experimentscited by 16
  12. 2020mpNet: variable depth unfolded neural network for massive MIMO channel estimationcited by 14
5 more
  1. 2022Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel chartingcited by 10
  2. 2021Efficient channel charting via phase-insensitive distance computationcited by 8
  3. 2022Efficient Deep Unfolding for SISO-OFDM Channel Estimationcited by 8
  4. 2021Deep learning for location based beamforming with NLOS channelscited by 7
  5. 2021Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizercited by 5

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2023Optimizing Multicarrier Multiantenna Systems for LoS Channel Chartingcited by 5
  2. 2023Model-based learning for location-to-channel mappingcited by 4
  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. 2024CSI Compression using Channel Chartingcited by 1