# Low Complexity Channel estimation with Neural Network Solutions

arXiv: https://arxiv.org/abs/2201.09934 · HTML: https://mlsanity.com/paper/2201.09934/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [3D Human Pose, Shape and Texture from Low-Resolution Images and Videos](https://mlsanity.com/paper/2103.06498/) (2021)

## Built on by (most cited first, 9 of 9)

- [Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training](https://mlsanity.com/paper/2302.04368/) (2023)
- [Attention Based Neural Networks for Wireless Channel Estimation](https://mlsanity.com/paper/2204.13465/) (2022)
- [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)
- [Achieving Robust Channel Estimation Neural Networks by Designed Training Data](https://mlsanity.com/paper/2507.12630/) (2025)
- [SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation](https://mlsanity.com/paper/2605.01931/) (2026)
- [CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation](https://mlsanity.com/paper/2306.13761/) (2023)
- [Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference](https://mlsanity.com/paper/2506.00452/) (2025)
- [Hybrid Mamba-Attention Neural Architecture for Channel Estimation](https://mlsanity.com/paper/2601.17108/) (2026)

## Abstract (arXiv)

Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario. Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost. The proposed method is more easily adapted to different pilot patterns and packet sizes. Compared with other deep learning methods for channel estimation, our results for 3GPP channel models suggest improved mean squared error performance for our approach.