4.6 Article

A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 70595-70619

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3188111

Keywords

Channel estimation; Symbols; OFDM; Wireless communication; Deep learning; Estimation; Dispersion; Artificial neural networks; channel estimation; convolutional neural networks; deep learning; Doppler effect; feedforward neural networks; frequency-selective channels; long short term memory; time-varying channels

Funding

  1. CY INEX through the ASIA Chair of Excellence [PIA/ANR-16-IDEX-0008]

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This paper discusses the use of deep learning for channel estimation in wireless communication systems affected by multipath fading and Doppler shift in dynamic environments, with extensive experimental simulations and evaluation of estimator performance in various mobility scenarios after considering different parameters. The study presents a comprehensive survey on channel estimation techniques based on deep learning and provides source codes online for reproducibility.
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.

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