Modulation Recognition Algorithm By Usign Parallel CNN-LSTM

To improve the performance of modulation type recognition algorithm based on convolutional neural network, CNN-LSTM parallel network is designed by CNN spatial feature extraction ability and LSTM time series feature extraction ability. The upper branch consists of a pooling layer and a convolution layer, and the lower branch uses a single-layer LSTM network. The in-phase component and quadrature component are directly used as input data, and the upper and lower branches extract the spatial and temporal characteristics of the signal respectively, to improve the feature expression ability. The experimental results of modulation type recognition for 7 kinds of signals, such as BPSK, QPSK, 8PSK, 16QAM, 32QAM, 16APSK and 32APSK, show that the algorithm does not need to artificially design the characteristic parameters and reduces the influence of human factors. At the same time, the algorithm has good recognition performance at lower SNR.