## 3 Experimental Verification and Discussions
When it comes to 32-QAM, the constellation becomes blurred, but obvious peak value can be seen by the AH method, which means that the modulation format can be well recognized, as shown in Figure 4b. When the modulation formats are 64-QAM and 128-QAM, not only the constellation map is blurred, but also there is no obvious peak value in the AH map. Therefore, KNN is used to process data to get the correct modulation format. As shown in Figure 4b, the shape of AH when RoP is \(-11\) dBm is different from that when RoP is \(-6\) dBm. Thus, as long as the corresponding OSNR under each RoP is known, OSNR value can be predicted according to different AH under different RoP. Consider that KNN has the function of regression prediction [25; 26], which can be used in achieving OSNR monitoring [18]. Since identification accuracy is affected by the number of training samples and the \(k\) value of the KNN algorithm, two different sets of samples and \(k\) value, being 30, 1 (case 1) and 60, 3 (case 2) are introduced in the construction of the classifier. The accuracy identification for 4 different sets of bit loading profiles while maintaining the same bit rate under case 1 and case 2 are shown in Figure 5. In general, the accuracy increases with the increase in the number of samples and the \(k\) value. Besides, there is no exception in this measurement. However, the improvement in accuracy is not significantly obvious as the performance of case 2 (dotted line) is only slightly improved at the RoP of \(<\)\(-12\) dBm and cannot reach 100%. Once the RoP reaches \(-12\) dBm or higher, the performance of the two mentioned cases will be the same. This suggests that the number of training samples and the \(k\) value are not the main influencing factors of the proposed algorithm. To increase the transmission efficiency, the number of training samples is 30, and the \(k\) value is 1 in the proposed KNN training model during the measurements. As shown in Figure 4, when RoP is \(-11\) dBm, the constellation map is already blurred. However, the identification accuracy of this algorithm can reach 100%, which shows that the algorithm also has a good recognition effect in the case of a relatively high error rate. Subsequently, the above measurement is repeated under different RoP for both optical back to back (OBTB) and 25 km SSMF configurations. The experimental results involving the mentioned 4 sets of different bit loading profiles are shown in Figure 6, in which the BERs under each bit loading profile is also plotted. Note that the identification accuracy of proposed MFI increases with the increase in RoP. Almost the same performance is obtained for 25 km SSMF transmissions compared to the BTB case, which shows that fiber dispersion does not significantly affect BER and the proposed MFI for current system configuration. This is mainly because our focus in this paper is on the algorithm verification for different types of modulation format, the signal rate involved in our experiment is not very high, and the chromatic dispersion induced system degradation is relatively small. For both 25 km SSMF and OBTB cases, 100% accurate identification can be achieved when the RoP is higher than \(-11\) dBm. Figure 3: Subcarrier bit allocation profile for orthogonal frequency division multiplexing (OFDM) signals. From Figure 6, identification accuracy depends on the receiving optical power, that is, the signal-to-noise ratio. Employment of a stronger equalization algorithm can improve the BER performance and also should improve the identification accuracy at low optical power regions. In practical deployment, the appropriate algorithm can be selected according to the requirements to achieve a balance between performance and complexity. In practical application, constellation rotation after equalization affects the performance of MFI performance. Further experimental investigation is undertaken to assess the effects of constellation rotation.