Abstract
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.
Highlights
Applications such as the Internet of Vehicles [1], cloud computing and flow media bring tremendous challenges to optical networks, especially in terms of bandwidth and flexibility [2]
A blind modulation format identification (MFI) based on principal component analysis and singular value decomposition is proposed for the identification of seven modulation formats of binary phase shift keying (BPSK), quadrature phase-shift keying (QPSK), 8PSK and 8/16/32/64QAM
Due to the proposed correcting method based on principal component analysis (PCA), the phase information is available to improve the accuracy of identification, and to extend our method to the scenario even with multiple PSK modulation formats, which will be unavailable in the MFI methods based on amplitude information
Summary
Applications such as the Internet of Vehicles [1], cloud computing and flow media bring tremendous challenges to optical networks, especially in terms of bandwidth and flexibility [2]. Aided by RF data, MFI based on frequency offset loading are proposed in [5,6] These methods often achieve excellent modulation format independence identify performance. An MFI method based on deep neural networks (DNNs) combined with amplitude histograms is proposed in [21], realizing the identification of QPSK, 16QAM and 64QAM. A blind MFI based on principal component analysis and singular value decomposition is proposed for the identification of seven modulation formats of BPSK, QPSK, 8PSK and 8/16/32/64QAM. Due to the proposed correcting method based on PCA, the phase information is available to improve the accuracy of identification, and to extend our method to the scenario even with multiple PSK modulation formats, which will be unavailable in the MFI methods based on amplitude information.
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