Blind Modulation Recognition of MIMO-OFDM System Based on Multi-terminal Feature Fusion Model

‍ ‍Automatic Modulation Classification （AMC） plays an important role in improving spectrum efficiency in cognitive radio. However， most of the existing work focuses on single carrier communication in single input single output system. Aiming at the problem of Blind Modulation Recognition of subcarriers in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing （MIMO-OFDM） system in non-cooperative communication， a blind modulation recognition method based on multi terminal feature fusion model is proposed in this paper. Firstly， the joint approximate diagonalization algorithm （JADE） of the characteristic matrix is used to recover the transmitted signal from the mixed signal at the receiver. Then， the cyclic spectral profile and co directional orthogonal component of the recovered signal are extracted as shallow features. Finally， the multi terminal feature fusion model is built， the shallow feature extraction and mapping are completed by using the series model of one-dimensional convolutional network （1D-CNN） and channel attention module （CAM）， and the proposed modulation recognition algorithm is simulated and verified by test samples. Simulation results show that this method can effectively identify the modulation mode of MIMO-OFDM system without priori informations， and the recognition accuracy can reach 90% when the signal-to-noise ratio is 4 dB.