# A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments

arXiv: https://arxiv.org/abs/2206.02165 · HTML: https://mlsanity.com/paper/2206.02165/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [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)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration](https://mlsanity.com/paper/1807.06766/) (2018)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications](https://mlsanity.com/paper/2104.08813/) (2021)
- [Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11p Standard](https://mlsanity.com/paper/2106.04829/) (2021)
- [Extended GFDM Framework: OTFS and GFDM Comparison](https://mlsanity.com/paper/1808.01161/) (2018)
- [Machine Learning Based Channel Modeling for Vehicular Visible Light Communication](https://mlsanity.com/paper/2002.03774/) (2020)

## Built on by (most cited first, 6 of 6)

- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Towards Explainable AI for Channel Estimation in Wireless Communications](https://mlsanity.com/paper/2307.00952/) (2023)
- [Deep Learning Based Channel Estimation in High Mobility Communications Using Bi-RNN Networks](https://mlsanity.com/paper/2305.00208/) (2023)
- [Explainable AI for Enhancing Efficiency of DL-based Channel Estimation](https://mlsanity.com/paper/2407.07009/) (2024)
- [X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation](https://mlsanity.com/paper/2602.22277/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)

## Abstract (arXiv)

Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task. Only a few pilots are used for channel estimation in conventional approaches to preserve high data rate transmission. Consequently, such estimators experience a significant performance degradation in high mobility scenarios. Recently, deep learning has been employed for doubly-dispersive channel estimation due to its low-complexity, robustness, and good generalization ability. Against this backdrop, the current paper presents a comprehensive survey on channel estimation techniques based on deep learning by deeply investigating different methods. The study also provides extensive experimental simulations followed by a computational complexity analysis. After considering different parameters such as modulation order, mobility, frame length, and deep learning architecture, the performance of the studied estimators is evaluated in several mobility scenarios. In addition, the source codes are made available online in order to make the results reproducible.