## 3 Results and Discussion
With the deterministic generation of frequency combs, we validate the machine learning system that classifies the required comb type to attend to network requests. The results of the Monte Carlo experiment performed in this work are presented in Table 3, which shows the confusion matrix diagonals that correspond to the accuracy percentages of each of the studied classification methods. Note that the SVM method efficiently classifies the four types of combs with accuracy percentages greater than 97%. The result of this classification indicates that the method can reliably identify the required comb type for the network. Additionally, other classification methods show statistically similar performance to that evidenced by the SVM, indicating that the computed characteristics appropriately describe the separation space. We choose the SVM as the approach for classification due to its lower computational cost, which is a fundamental factor in carrying out dynamic spectrum assignment. Regarding these results, the classification error of approximately 3% can be ascribed to various factors. Firstly, specific application scenarios inherently pose complexities in classification due to their distinctiveness and resemblance to adjacent classes. Furthermore, data noise also contributes to this margin of error. Lastly, inherent limitations within the chosen model's architecture may affect its capacity to confront the data's challenges. The amalgamation of these factors leads to the observed 3% error and emphasizes the significance of exploring novel classification model proposals. Note that CNNs exhibit poorer performance than classical learning methods. This result arises due to the database's tabular nature, making it challenging for CNNs to adapt as effectively as classical methods to such scenarios [40]. We can observe that the CNN model is over-fitted. This issue is attributed to the tabular feature and the insufficient data in the database, rendering it unsuitable for deep learning approaches. Figure 10: (**a**) Repetition histogram for the control strategy using the 113 μm MRR. (**b**) Repetition histogram for the control strategy using the 450 μm MRR. (**c**) Npeak histogram of 200 simulations for 113 μm and 450 μm MRR. Figure 9: (**a**) Pump power (blue) and detuning (red) vs. slow time (t) using 450 μm. (**b**) Optical intensity using 450 μm. (**c**) Optical spectrum using 450 μm. (**d**) Histogram of the number of peaks (Npeak) for 200 simulations using 450 μm. (**e**) Pump power (blue) and detuning (red) vs. slow time (t) using 113 μm. (**f**) Optical intensity using 113 μm. (**g**) Optical spectrum using 113 μm. (**h**) Histogram of the number of peaks (Npeak) for 200 simulations using 113 μm. For the validation phase of the allocation algorithm, we propose a scenario that includes eleven wavelengths of an OFC with an FSR of 50 GHz and seven requests. This experiment aims to improve our understanding of the allocation process and algorithm functioning. In order to assign lines, we propose using a multi-level quadrature amplitude modulation (QAM) format, which allows for modulation levels of 16, 32, and 64, thereby ensuring the attainment of varying bandwidth levels within the limitations of distance. The first user (R1) requests 450 Gbit/s at 40 km; the second user (R2) requests 200 Gbit/s at 50 km; the third user (R3) requests 100 Gbit/s at 50 km; and the fourth (R4), fifth (R5), sixth (R6), and seventh (R7) users request 50 Gbit/s at distances of 50 km and 75 km. Figure 11 shows the OFC wavelengths, their kilometer capacities, and the results of spectrum assignment for the requests. Note that our method assigns each request in a modulation format that guarantees the minimum transmission distance. Furthermore, note that requests 1 and 2 use super-channels to support modulation. This result verifies that the algorithm responds to the ideas proposed for the assignment.