高速移动环境下基于深度学习的信道估计方法

针对高速移动环境下信道快时变、非平稳特性导致下行链路信道估计性能受限的问题，本文提出一种基于深度学习的信道估计网络，即ChanEstNet.ChanEstNet使用卷积神经网络（Convolutional Neural Network，CNN）提取信道响应特征矢量和循环神经网络（Recurrent Neural Network，RNN）进行信道估计.我们利用标准的高速信道数据对学习网络进行离线训练，充分挖掘训练样本中的信道信息，使其学习到高速移动环境下信道快时变和非平稳的特点，更好的跟踪高速环境下信道的变化特征.仿真结果表明，在高速移动环境下，与传统方法相比，所提信道估计方法计算复杂度低，性能提升明显.

Channel Estimation Method Based on Deep Learning in High-Speed Mobile Environments

Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile environment,this paper proposes a channel estimation network based on deep learning,called ChanEstNet.ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation.We use the standard high-speed channel data to conduct offline training for the learning network,fully excavate the channel information in the training sample,make it learn the characteristics of fast time-varying and non-stationary channels in high-speed mobile environments,and better track the characteristics of channel changing in high-speed environment.The simulation results show that in the high-speed mobile environment,compared with the traditional methods,the proposed channel estimation method has low computational complexity and significant performance improvement.