NDLI: Blind Modulation Format Identification Based on Principal Component Analysis and Singular Value Decomposition

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Blind Modulation Format Identification Based on Principal Component Analysis and Singular Value Decomposition

Content Provider	MDPI
Author	Jiang, Jinkun Zhang, Qi Xin, Xiangjun Gao, Ran Wang, Xishuo Tian, Feng Tian, Qinghua Liu, Bingchun Wang, Yongjun
Copyright Year	2022
Description	As optical networks evolve towards flexibility and heterogeneity, various modulation formats are used to match different bandwidth requirements and channel conditions. For correct reception and efficient compensation, modulation format identification (MFI) becomes a critical issue. Thus, a novel blind MFI method based on principal component analysis (PCA) and singular value decomposition (SVD) is proposed. Based on square operation and PCA, the influence of phase rotation is removed, which avoids phase rotation-related discussions and training. By performing SVD on the density matrix about constellation, a denoise method is implemented and the quality of the constellation is improved. In the subsequent processing, the denoised density matrix is used as the feature of the support vector machine (SVM), and the identification of seven modulation formats such as BPSK, QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM is realized. The results show that lower OSNR values are required for the 100% accurate identification of all modulation formats to be achieved, which are 5 dB, 7 dB, 8 dB, 11 dB, 14 dB, 14 dB and 15 dB. Moreover, the proposed method still retains the advantage, even when the number of samples decrease, which is beneficial for low-complexity implementation.
Starting Page	612
e-ISSN	20799292
DOI	10.3390/electronics11040612
Journal	Electronics
Issue Number	4
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2022-02-16
Access Restriction	Open
Subject Keyword	Electronics Optical Communication Digital Signal Processing Modulation Format Identification Principal Component Analysis Singular Value Decomposition Support Vector Machine
Content Type	Text
Resource Type	Article

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