# Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel

arXiv: https://arxiv.org/abs/2411.15589 · HTML: https://mlsanity.com/paper/2411.15589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency](https://mlsanity.com/paper/1905.03761/) (2019)
- [Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding](https://mlsanity.com/paper/1901.06537/) (2019)
- [ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions](https://mlsanity.com/paper/2002.02445/) (2020)
- [An Overview of Signal Processing Techniques for Terahertz Communications](https://mlsanity.com/paper/2005.13176/) (2020)

## Abstract (arXiv)

An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an efficient convolutional neural network (CNN) based THz channel estimator that estimates the THz channel factors using uplink sub-6GHz channel. Further, we use the estimated THz channel factors to predict the optimal beamformer from a pre-given codebook, using a dense neural network. We not only get rid of the overhead associated with the conventional methods, but also achieve near-optimal spectral efficiency rates using the proposed beamformer predictor. The proposed method also outperforms deep learning based beamformer predictors accepting THz channel matrices as input, thus proving the validity and efficiency of our sub-6GHz based approach.