# Channel charting based beamforming

arXiv: https://arxiv.org/abs/2212.12340 · HTML: https://mlsanity.com/paper/2212.12340/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency](https://mlsanity.com/paper/1905.03761/) (2019)
- [Triplet-Based Wireless Channel Charting: Architecture and Experiments](https://mlsanity.com/paper/2005.12242/) (2020)
- [mpNet: variable depth unfolded neural network for massive MIMO channel estimation](https://mlsanity.com/paper/2008.04088/) (2020)
- [Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting](https://mlsanity.com/paper/2204.13996/) (2022)
- [Efficient channel charting via phase-insensitive distance computation](https://mlsanity.com/paper/2104.13184/) (2021)
- [Efficient Deep Unfolding for SISO-OFDM Channel Estimation](https://mlsanity.com/paper/2210.06588/) (2022)
- [Deep learning for location based beamforming with NLOS channels](https://mlsanity.com/paper/2201.01386/) (2021)
- [Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer](https://mlsanity.com/paper/2110.11279/) (2021)

## Built on by (most cited first, 5 of 5)

- [Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting](https://mlsanity.com/paper/2310.03762/) (2023)
- [Model-based learning for location-to-channel mapping](https://mlsanity.com/paper/2308.14370/) (2023)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [Model-based learning for multi-antenna multi-frequency location-to-channel mapping](https://mlsanity.com/paper/2407.07719/) (2024)
- [CSI Compression using Channel Charting](https://mlsanity.com/paper/2501.01431/) (2024)

## 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.