# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
## 2 Theory and Principle
### Results and Discussion  
The simulation system generated 9100 constellation diagrams in PNG format, which have a pixel size of \(512\times 512\). The sample constellation diagram obtained after 80 km SMF transmission is shown in Figure 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Type** & **Value** \\ \hline Central wavelength & 193.1 THZ \\ Signal Rate & 30 GBaud \\ Fiber input power & 10 dBm \\ Optical amplifier & EDFA \\ EDFA gain & 16 dB, 24 dB \\ EDFA noise & 4 dB \\ Fiber & SMF \\ Fiber attenuation coefficient & 0.2 dB/km \\ Fiber nonlinear coefficient & 1.31 (W-km)\({}^{-1}\) \\ Fiber dispersion coefficient & 16.75 ps/(nm-km) \\ Fiber distance & 80 km, 120 km \\ OSNR range & 0 dB\(\sim\)30 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cohort optical fiber communication system parameters. Figure 3: Simulation system setup. In Figure 5, the three types of signals, QPSK, 8 QAM, and 256 QAM, have high identification accuracy of 99% in the overall case and at low OSNR, with the identification accuracy of QPSK and 8 QAM signals reaching 100%. The 16 QAM, 32 QAM, 64 QAM, and 128 QAM signals have the possibility of misidentification among each other. However, in the MFI scheme proposed in this paper, the overall identification accuracy of 16 QAM, 32 QAM, 64 QAM, and 128 QAM reaches 98.2%, 98.5%, 95.1%, and 98.2%, respectively, and the identification accuracy at low OSNR reaches 95.5%, 96%, 87.3%, and 95.3%, respectively. In Figure 6, the three types of signals, QPSK, 8 QAM, and 32 QAM, have high identification accuracy of 98% in the overall case and at low OSNR, with the identification accuracy of QPSK and 8 QAM signals reaching 100%. The 16 QAM, 64 QAM, 128 QAM, and 256 QAM signals have the possibility of misidentification among each other. However, in the MFI scheme proposed in this paper, the overall identification accuracy of 16 QAM, 64 QAM, 128 QAM, and 256 QAM reaches 97.4%, 95.9%, 97.4%, and 98.2%, respectively, and the identification accuracy at low OSNR reaches 93.3%, 89.3%, 93.3%, and 95.3%, respectively. In Figure 7, the identification accuracy of the seven types of signals gradually increases as the OSNR increases until they all reach 100%. Among them, the identification accuracy of QPSK and 8 QAM is always 100%. In Figure 7a, the OSNR values of 16 QAM, 32 QAM, 64 QAM, 128 QAM, and 256 QAM when reaching 100% identification accuracy are around 5 dB, 2.5 dB, 7.5 dB, 10 dB, and 2.5 dB, respectively. In Figure 7b, the OSNR values of 16 QAM, 32 QAM, 64 QAM, 128 QAM, and 256 QAM at 100% identification accuracy are around 5 dB, 2.5 dB, 5 dB, 10 dB, and 7.5 dB, respectively. The above analysis shows that the proposed MFI scheme can significantly improve the signal identification accuracy, thus improving the classifier's performance. Figure 6: The average identification accuracy after 120 km fiber transmission (**a**) OSNR range is 0\(\sim\)30 dB (**b**) OSNR range is 0\(\sim\)10 dB. Figure 7: Comparison of seven types of signal identification accuracy. The fiber distance is set as (**a**) 80 km, (**b**) 120 km. To verify the superiority of the proposed MFI scheme in this paper, different MFI schemes are compared at fiber lengths of (a) 80 km and (b) 120 km, as depicted in Figure 8. The results show that the proposed MFI scheme quickly achieves 100% overall identification accuracy and remains stable when OSNR \(\geq\) 10 dB. High overall identification accuracy is also achieved at low OSNR. Because of the influence of high-order QAM signals in seven kinds of signals, such as 64, 128, and 256 QAM, the overall identification accuracy of GLCM features and FD features is not good. Although the overall identification accuracy of MFI based on HOS features is high, it fails to achieve 100% identification accuracy within the set OSNR. In summary, the proposed MFI scheme has better stability and superiority compared with other MFI schemes.