一种适用多种空时编码方案的MIMO自编码器通信系统

为提升现有端到端通信系统的适应性与信道估计的准确性,提出了一种适用于多种空时编码方案的多输入多输出(Multiple-Input Multiple-Output,MIMO)自编码器通信系统.该系统将基于卷积神经网络的自编码器引入到MIMO系统中,并结合信道估计网络实现信道均衡,通过端到端的学习方式实现各种空时编码方案下...

A MIMO autoencoder communication system suitable for multiple space-time coding schemes

In order to improve the adaptive capacity of existing end-to-end communication systems and the accuracy of channel estimation methods,a multiple-input multiple-output(MIMO) autoencoder(AE) communication system suitable for multiple space-time coding schemes is proposed.This AE-MIMO system introduces the autoencoder based on the convolutional neural network into the MIMO system,and combines the channel estimation network to achieve channel equalization.This system implements signal mapping and transmission under various space-time coding schemes through end-to-end learning to obtain the best receiving performance.Simulation experiments show that the proposed system has good adaptability and generalization ability under different channel environments,modulation methods and space-time coding schemes.Moreover,under the vertical space-time layered coding scheme,the proposed system can match the maximum likehood detection performance in the case of low signal-to-noise ratio(SNR),and obtain better signal detection performance than traditional nonlinear methods in the case of high SNR.Compared with the traditional method,the proposed channel estimation method obtains a performance improvement of about 5.5 dB under long term evolution extended typical urban model.