Journal on Communications ›› 2021, Vol. 42 ›› Issue (7): 84-94.doi: 10.11959/j.issn.1000-436x.2021142

• Papers • Previous Articles     Next Articles

Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal

Zeliang AN1, Tianqi ZHANG1, Baoze MA1, Pan DENG1, Yuqing XU2   

  1. 1 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    2 School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Revised:2021-03-01 Online:2021-07-25 Published:2021-07-01
  • Supported by:
    The National Natural Science Foundation of China(61671095);The National Natural Science Foundation of China(61702065);The National Natural Science Foundation of China(61701067)


To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.

Key words: modulation recognition, MIMO-OSTBC, zero-forcing blind equalization, 1D-CNN, decision fusion

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