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  • Received: Apr. 29, 2020

    Accepted: Aug. 18, 2020

    Posted: Sep. 27, 2020

    Published Online: Sep. 29, 2020

    The Author Email: Qi Zhang (zhangqi@bupt.edu.cn)

    DOI: 10.3788/COL202018.111404

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    Shanshan Li, Qi Zhang, Xiangjun Xin, Ran Gao, Sitong Zhou, Ying Tao, Yufei Shen, Huan Chang, Qinghua Tian, Feng Tian, Yongjun Wang, Leijing Yang. No prior recognition method of modulation mode by partition-fractal and SVM learning method[J]. Chinese Optics Letters, 2020, 18(11): 111404

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Chinese Optics Letters, Vol. 18, Issue 11, 111404 (2020)

No prior recognition method of modulation mode by partition-fractal and SVM learning method

Shanshan Li1, Qi Zhang1,2,3,*, Xiangjun Xin1,2,3, Ran Gao4, Sitong Zhou1, Ying Tao5, Yufei Shen6, Huan Chang7, Qinghua Tian1,2,3, Feng Tian1,2,3, Yongjun Wang1,2,3, and Leijing Yang1,2,3

Author Affiliations

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China
  • 2Beijing Key Laboratory of Space-round Interconnection and Convergence, BUPT, Beijing 100876, China
  • 3State Key Laboratory of Information Photonics and Optical Communications, BUPT, Beijing 100876, China
  • 4The Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
  • 5China Academy of Space Technology, Beijing 100094, China
  • 6China Satellite Communication Co., Ltd., Beijing 100048, China
  • 7School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Abstract

A modulation classification method in combination with partition-fractal and support-vector machine (SVM) learning methods is proposed to realize no prior recognition of the modulation mode in satellite laser communication systems. The effectiveness and accuracy of this method are verified under nine modulation modes and compared with other learning algorithms. The simulation results show when the signal-to-noise ratio (SNR) of the modulated signal is more than 8 dB, the classifier accuracy based on the proposed method can achieve more than 98%, especially when in binary phase shift keying and quadrature amplitude shift keying modes, and the classifier achieves 100% identification whatever the SNR changes to. In addition, the proposed method has strong scalability to achieve more modulation mode identification in the future.

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