## 3 Results and Discussion
Subsequently, we propose an experiment with 1400 request scenarios based on the variations in Table 1. The goal of this experiment is to compute the performance of the proposed method. To carry out the assignment, we employ an SVM to select the appropriate OFC and apply the proposed RMLSA algorithm. These request scenarios can simultaneously accommodate up to 200 requests, with a maximum bandwidth of 31 THz. Figure 12 displays the number of rejections obtained by the assignment algorithm. Notably, increasing the number of requests and their respective bandwidths leads to a corresponding increase in rejections (see Figure 12a). This observation emphasizes the importance of maintaining the maximum number of available carriers to prevent these rejections. Furthermore, rejected requests are more likely to have large bandwidths because a request of such magnitude requires a super-channel, significantly reducing the number of available carriers. Despite encountering rejections during the exercise, it is important to note that the rejection number is minimal. Specifically, out of the 1400 request scenarios, 1060 could be addressed using the proposed method (see Figure 12b). These findings imply that the proposed method is reliable when facing real-world network situations. It is important to emphasize that the allocation of users relies on three factors: the request  
Figure 11: Spectrum assignment using the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **SVM** & **ANN** & **KNN** & **CNN-1D** \\ \hline
12.5 GHz & \(98.86\pm 0.19\) & \(98.70\pm 0.31\) & \(94.03\pm 3.11\) & \(97.66\pm 2.27\) \\
50 GHz & \(99.71\pm 0.30\) & \(98.27\pm 1.81\) & \(93.11\pm 3.75\) & \(85.03\pm 4.20\) \\
100 GHz & \(99.76\pm 0.25\) & \(97.91\pm 2.19\) & \(97.05\pm 2.39\) & \(86.03\pm 4.31\) \\
200 GHz & \(98.72\pm 2.48\) & \(98.92\pm 1.12\) & \(96.41\pm 2.89\) & \(87.45\pm 3.78\) \\ ine Total GHz & \(99.26\pm 0.43\) & \(98.45\pm 0.92\) & \(95.37\pm 2.32\) & \(89.04\pm 3.64\) \\ ine Time GHz & \(28\pm 2.7\) (ms) & \(46.6\pm 4.6\) (ms) & \(33\pm 4.1\) (ms) & \(48\pm 7.1\) (ms) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of classification methods for OFC selection. bandwidth, the quantity of carriers accessible for assignment, and the transmission distance of each user. Bandwidth demands may necessitate a substantial quantity of carriers to construct a super-channel, consequently diminishing the pool of carriers available for other users. Moreover, not all carriers can meet quality of service (QoS) requirements due to the sech2 profile of the frequency comb. Consequently, when customers are situated at significantly extended distances, the likelihood of their requests encountering rejection elevates. Hence, maximizing the amount of available spectral components is imperative to enhance the network allocation performance. Another important metric to measure network efficiency is BBR; this metric quantifies the likelihood of the network being blocked due to a bandwidth that exceeds the assignable limit. Figure 13a shows the BBR calculation for each network request. As with the previous figures, there is a higher probability of blocking when requests with many customers and large bandwidths exist. However, Figure 13b shows that our method successfully addresses 1090 requests with a blocking probability of zero, accounting for 81% of the total requests. This result demonstrates that our method exhibits adequate adaptability to this type of demand. To compare our methodology with other state-of-the-art approaches, we implement the FF and RWA methods [41; 45] in a WDM scenario. These methods operate with a fixed number of wavelengths and attempt to minimize the blocking probability. However, the FF and RWA methods utilize multiple carriers generated from individual lasers, significantly fewer than those obtained from an optical frequency comb. This limitation occurs due to the cost of implementing the network, which increases with the number of lasers.