# Attention Based Neural Networks for Wireless Channel Estimation

arXiv: https://arxiv.org/abs/2204.13465 · HTML: https://mlsanity.com/paper/2204.13465/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems](https://mlsanity.com/paper/2006.11796/) (2020)
- [Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks](https://mlsanity.com/paper/2010.12004/) (2020)
- [An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation](https://mlsanity.com/paper/2108.09430/) (2021)
- [Low Complexity Channel estimation with Neural Network Solutions](https://mlsanity.com/paper/2201.09934/) (2022)
- [Deep Learning Based OFDM Channel Estimation Using Frequency-Time Division and Attention Mechanism](https://mlsanity.com/paper/2107.07161/) (2021)
- [OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation](https://mlsanity.com/paper/2002.10493/) (2020)

## Built on by (most cited first, 11 of 11)

- [Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training](https://mlsanity.com/paper/2302.04368/) (2023)
- [Achieving Robust Generalization for Wireless Channel Estimation Neural Networks by Designed Training Data](https://mlsanity.com/paper/2302.02302/) (2023)
- [Robust Channel Estimation for Optical Wireless Communications Using Neural Network](https://mlsanity.com/paper/2504.02134/) (2025)
- [Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems](https://mlsanity.com/paper/2306.06125/) (2023)
- [Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels](https://mlsanity.com/paper/2504.11667/) (2025)
- [AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation](https://mlsanity.com/paper/2505.09076/) (2025)
- [Achieving Robust Channel Estimation Neural Networks by Designed Training Data](https://mlsanity.com/paper/2507.12630/) (2025)
- [ReQuestNet: A Foundational Learning model for Channel Estimation](https://mlsanity.com/paper/2508.08790/) (2025)
- [Learn to Adapt to New Environment from Past Experience and Few Pilot](https://mlsanity.com/paper/2209.02649/) (2022)
- [Hybrid Mamba-Attention Neural Architecture for Channel Estimation](https://mlsanity.com/paper/2601.17108/) (2026)
- [Brownian Bridge Diffusion-Based Joint Channel Estimation and Data Detection for Jamming-Resilient Receivers](https://mlsanity.com/paper/2606.28778/) (2026)

## Abstract (arXiv)

In this paper, we deploy the self-attention mechanism to achieve improved channel estimation for orthogonal frequency-division multiplexing waveforms in the downlink. Specifically, we propose a new hybrid encoder-decoder structure (called HA02) for the first time which exploits the attention mechanism to focus on the most important input information. In particular, we implement a transformer encoder block as the encoder to achieve the sparsity in the input features and a residual neural network as the decoder respectively, inspired by the success of the attention mechanism. Using 3GPP channel models, our simulations show superior estimation performance compared with other candidate neural network methods for channel estimation.