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.

In order to achieve automatic identification of modulation formats in orthogonal frequency division multiplexing (OFDM) subcarrier signals, a recognition method based on multiple feature inputs and a Hybrid Training Neural Network (HTNN) is proposed, in which an HTNN structure is designed to obtain high-order statistical correlation features and constellations of OFDM subcarriers. The recognition performance of the proposed method in free space channel transmission and atmospheric time-varying channel transmission is studied by simulation. Simulation results show that the overall identification accuracy of the recognition model based on the proposed method exceeded 93.37% in the wide Signal-to-Noise Ratio (SNR) range of the free space channel. With an SNR higher than 7.5 dB, identification accuracy performance of the learning model culminated, achieving 100% identification accuracy of OFDM subcarrier signals. Under weak turbulent atmospheric and time-varying channel conditions, the overall identification accuracy curve tended to increase as SNR increased and stabilized at more than 95%. Compared with the two comparison methods, the proposed method reduced the sensitivity to channel variations and improved the convergence speed on the basis of the guaranteed identification accuracy, and enabled reliable identification of OFDM subcarrier signals in a wide SNR range.

In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR ≥ 10 dB.

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This paper proposes a novel orthogonal frequency division multiplexing (OFDM) optical access scheme based on bit reconstruction. In this method, correlation is introduced into the data information of optical line terminals (OLT) through the logical coding circuits and partition mapping. Even after passing through the optical fibre channel, the strong correlation after bit reconstruction can still be used in the optical network unit (ONU) for reliable decoding. In the simulation experiments, a 60 Gbit/s bit reconstruction 64 quadrature amplitude modulation (QAM) OFDM signal was successfully transmitted over a 10/20 km single-mode fibre (SMF). The simulation results show that the proposed scheme can effectively achieve reliable transmission with gains of about 1.3 dB and 0.51 dB at a 20% soft decision-forward error correction (SD-FEC) threshold, respectively. The proposed scheme is a promising candidate for a next-generation passive optical network (NGPON) solution.