Serial Sequence Space-Time Block Code Recognition Method By Using Convolutional-Recurrent Neural Networks#br#

 Aiming at the space-time block code recognition problem of multiple input multiple output system, a method of space-time block code recognition for serial sequences by using convolutional cyclic neural network is proposed. The real and imaginary parts of the one-dimensional received signal are separated into the network, and the spatial characteristics are extracted by the convolutional neural network, and the deep-seated temporal characteristics are extracted by the recurrent neural network, so as to improve the characteristic expression ability of the network. The network training process adopts the back propagation method to calculate the error between the output and the target value, send the error back to the network and update the weight to complete the network training. The test data is input into the trained network to realize the recognition of STBC code. This method applies the deep learning algorithm to the recognition of space-time block codes of serial sequences for the first time. The trained network can directly recognize space-time block codes under a single receiving antenna without the need to double calculate the statistical characteristics of the signal and avoid the artificial design of feature parameters and detection threshold. Simulation results show that the algorithm can recognize the space-time block codes of serial sequences and has good recognition performance under low SNR.