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.

本文针对动态环境下的无线通信系统中频道估计困难的问题，对基于深度学习的频道估计技术进行了广泛的研究和实验模拟，并提供了计算复杂度分析和在线源代码，最终通过在不同移动性情况下的表现评估来支持它们的可靠性和应用。

基于深度学习的双重色散环境下信道估计调查

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

We conduct an in depth study on the performance of deep learning based radio
signal classification for radio communications signals. We consider a rigorous
baseline method using higher order moments and strong boosted gradient tree
classification and compare performance between the two approaches across a
range of configurations and channel impairments. We consider the effects of
carrier frequency offset, symbol rate, and multi-path fading in simulation and
conduct over-the-air measurement of radio classification performance in the lab
using software radios and compare performance and training strategies for both.
Finally we conclude with a discussion of remaining problems, and design
considerations for using such techniques.

深度学习在无线电通信信号分类方面的性能与基线方法比较研究，研究了不同配置和信道失真情况下的分类性能，探讨了载波频率偏移、符号速率和多径衰落等因素对性能的影响，并使用软件无线电进行了室内分类性能的实测，最终讨论了该技术的应用存在的问题和设计考虑。