Laser & Optoelectronics Progress, Volume. 60, Issue 9, 0906009(2023)

Review on Artificial Intelligence Modulation Format Recognition Algorithm Based on Feature Extraction

Guojin Qin and Nan Chi*
Author Affiliations
  • The Ministry of Education Key Laboratory of Electromagnetic Wave Information Science, Department of Communication Science and Engineering, College of Information Science and Engineering, Fudan University, Shanghai 200433, China
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    Figures & Tables(6)
    System architecture of artificial intelligence modulation format recognition based on feature extraction
    Algorithm module of artificial intelligence modulation format recognition based on feature extraction
    Three SVM structures used in modulation format classification. (a) 1-VS-All SVM; (b) 1-VS-Rest SVM[22]; (c) binary tree SVM [10]; (d) linear and separable; (e) nonlinear and separable[11]
    Principle diagram of modulation format recognition using ANN combined with CWT and HOM[19]
    Principle diagram of modulation format recognition based on CNN combined with STFT spectrum[34], eye diagram[24] and cyclic spectrum[31]
    • Table 1. Conclusion of methods of artificial intelligence modulation format recognition based on feature extraction

      View table

      Table 1. Conclusion of methods of artificial intelligence modulation format recognition based on feature extraction

      FeatureDimensionClassifierConvenienceComplexity
      HOC0SVM/ANNCommonLow
      Instantaneous0SVM/ANNCommonLow
      CWT1SVM/ANNLowCommon
      MFCC1SVM/ANNLowCommon
      ADS1 or 2ANN/CNNLowCommon
      Eye Diagram2CNNHighHigh
      CS2CNNHighHigh
      STFT2CNNHighHigh
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    Guojin Qin, Nan Chi. Review on Artificial Intelligence Modulation Format Recognition Algorithm Based on Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(9): 0906009

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Mar. 21, 2022

    Accepted: May. 26, 2022

    Published Online: May. 9, 2023

    The Author Email: Chi Nan (nanchi@fudan.edu.cn)

    DOI:10.3788/LOP221055

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