# Deep OFDM Channel Estimation: Capturing Frequency Recurrence

arXiv: https://arxiv.org/abs/2401.05436 · HTML: https://mlsanity.com/paper/2401.05436/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 7 Jan 2024.

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

- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Neural Network Detection of Data Sequences in Communication Systems](https://mlsanity.com/paper/1802.02046/) (2018)
- [Wideband Channel Estimation with A Generative Adversarial Network](https://mlsanity.com/paper/2012.12063/) (2020)
- [Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels](https://mlsanity.com/paper/1908.11013/) (2019)
- [Deep Learning Based OFDM Channel Estimation Using Frequency-Time Division and Attention Mechanism](https://mlsanity.com/paper/2107.07161/) (2021)
- [Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates](https://mlsanity.com/paper/1912.11896/) (2019)

## Built on by (most cited first, 2 of 2)

- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)
- [AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation](https://mlsanity.com/paper/2505.09076/) (2025)

## Abstract (arXiv)

In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.