# Model-based Deep Learning for Beam Prediction based on a Channel Chart

arXiv: https://arxiv.org/abs/2312.02239 · HTML: https://mlsanity.com/paper/2312.02239/

## Key facts

- Cited by 4 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 4 Dec 2023.

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

- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Towards Understanding the Spectral Bias of Deep Learning](https://mlsanity.com/paper/1912.01198/) (2019)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [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)
- [Multilevel MIMO Detection with Deep Learning](https://mlsanity.com/paper/1812.01571/) (2018)
- [Deep learning for location based beamforming with NLOS channels](https://mlsanity.com/paper/2201.01386/) (2021)
- [Channel charting based beamforming](https://mlsanity.com/paper/2212.12340/) (2022)
- [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)
- [Neural network approaches to point lattice decoding](https://mlsanity.com/paper/2012.07032/) (2020)

## Built on by (most cited first, 4 of 4)

- [Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays](https://mlsanity.com/paper/2411.15144/) (2024)
- [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)
- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)

## Abstract (arXiv)

Channel charting builds a map of the radio environment in an unsupervised way. The obtained chart locations can be seen as low-dimensional compressed versions of channel state information that can be used for a wide variety of applications, including beam prediction. In non-standalone or cell-free systems, chart locations computed at a given base station can be transmitted to several other base stations (possibly operating at different frequency bands) for them to predict which beams to use. This potentially yields a dramatic reduction of the overhead due to channel estimation or beam management, since only the base station performing charting requires channel state information, the others directly predicting the beam from the chart location. In this paper, advanced model-based neural network architectures are proposed for both channel charting and beam prediction. The proposed methods are assessed on realistic synthetic channels, yielding promising results.