Joint baud-rate and modulation format identification based on asynchronous delay-tap plots analyzer by using convolutional neural network

Jie Gao, Xian Zhou, Jiahao Huo, Ke He, Kangping Zhong, Jinhui Yuan, Keping Long, Changyuan Yu, Alan Pak Tao Lau, Chao Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


In this paper, a joint baud-rate and modulation format identification (BR-MFI) is proposed based on asynchronous delay tap picture (ADTP) analyzer by using convolutional neural network (CNN). Considering 8 types of signals under different channel conditions of OSNR, CD and DGD, the proposed BR-MFI can achieve 100% accuracy after 6 training epochs, and just 2 epochs for MFI. Here, two test number of samples are about 15% of total samples. This paper also investigates the influence of CNN structure on the identification accuracy. The results show that CNN has better performance for image processing than back-propagation Artificial Neural Network (BP-ANN).

Original languageEnglish
Pages (from-to)97-102
Number of pages6
JournalOptics Communications
Publication statusPublished - 1 Nov 2019


  • Asynchronous delay tap picture
  • Baud-rate
  • Convolutional neural network
  • Modulation format identification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Physical and Theoretical Chemistry
  • Electrical and Electronic Engineering

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