Abstract
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 language | English |
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Pages (from-to) | 97-102 |
Number of pages | 6 |
Journal | Optics Communications |
Volume | 450 |
DOIs | |
Publication status | Published - 1 Nov 2019 |
Keywords
- 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