# Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network

arXiv: https://arxiv.org/abs/1911.13106 · HTML: https://mlsanity.com/paper/1911.13106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation](https://mlsanity.com/paper/2205.12445/) (2022)

## Abstract (arXiv)

In this paper, we propose a model-driven channel estimation method utilizing a convolutional neural network (CNN) derived from image super-resolution (SR). Instead of completely abandoning traditional communication modules as data-driven approaches always do, we first obtain rough channel matrix by the simplest least square (LS) channel estimation, and then use a CNN-based SR network to learn the subtle connections within channel matrix for accuracy improvement. The results show that our proposed method significantly outperforms the linear minimum mean squared error (LMMSE) estimation and has potential in spectrum saving. The impacts of network structure and SNR mismatch problem are also investigated in this paper.