这是一个使用深度学习工具箱中的长短期存储器（LSTM）网络在OFDM系统信号检测接收器上实现符号分类的例子。

This is an example of using the long short-term memory (LSTM) network in the Deep Learning Toolbox to achieve symbol classification at the receiver for signal detection in OFDM systems. 

基于LSTM的神经网络是针对单个子载波进行训练的，该神经网络计算符号误码率（SER），并与最小二乘（LS）和最小均方误差（MMSE）估计进行了比较。

The LSTM-based neural network is trained for a single subcarrier, where the symbol error rate (SER) is calculated and compared with the least square (LS) and minimum mean square error (MMSE) estimations. 

在初步研究的离线训练和在线部署阶段，假设无线信道是固定不变的。

The wireless channel is assumed to be fixed during the offline training and the online deployment stages in this initial investigation. 

为了测试神经网络的鲁棒性，对每一个发送的OFDM包应用随机相移。

To test the robustness of the neural network, a random phase shift is applied for each transmitted OFDM packet. 

考虑了导频符号个数和循环前缀长度的影响。

The impacts of the number of pilot symbols and the length of the cyclic prefix (CP) are considered. 

要重新创建仿真模拟结果，请加载相应的mat文件并运行Testing.m。

To recreate the simulation results, please load the corresponding mat file and run the script Testing.m.

参考文献：

H. Ye, G. Y. Li and B. Juang, "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems," in IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114-117, Feb. 2018.