## 3 Results and Discussion
Therefore, to allow a useful comparison with our design, we propose a scenario with 60 available carriers, which are all distributed according to the WDM-UIT proposal. In this distribution, all carriers have the same OSNR of 60 dB. Figure 12: (**a**) Rejection number vs. full bandwidth and request number. (**b**) Histogram for the rejection number obtained in each set of requests made by the network. Figure 13: (**a**) BBR vs. full bandwidth and request number. (**b**) Histogram for BBR obtained for each network request. In Figure 14, we can observe the result of computing the histogram for the calculation of BBR. We can observe that our method has a lower blocking probability compared to the FF and RWA methods. To verify this observation, we count the number of requests with a blocking probability greater than 0%, likely to have not been assigned, or to have a low probability of assignment. It is worth noting that the FF method has 518 requests with a blocking probability greater than 0%, and the RWA method has 519 requests with a probability greater than 0%, whereas our method has 310 requests. On the other hand, these methods show greater variability in the experiment, indicating that their blocking probability is higher for some scenarios, reaching values of up to 9%, resulting in inadequate network performance. This problem arises because our approach can use a more significant number of carriers and take advantage of the lines generated by the optical frequency comb. Our method allows for a reduction in the blocking probability and better adaptation to the proposed scenario. It is important to note that these methods can exhibit cases of blocking probability up to 8%, whereas our method only exhibits up to 1.5% probability. Although the percentage of unblocked requests is close to that in our method, the methodology proposed in this work only requires eight lasers to achieve this performance, which significantly reduces the implementation costs. This laser number is a fundamental factor because this device is one of the most expensive and complex in the network. Another test proposed to measure performance is to use the same number of lasers used by our approach for the FF and RWA methods, thus verifying the blocking probability. Note that the network performance drops significantly, obtaining 1126 requests with a blocking probability higher than zero for the FF method and 1129 requests for the RWA method (see Figure 15). This result justifies the clear advantage that our method has over these approaches. On the other hand, in some situations, the probability of blocking can reach up to 45%, which is excessively high for a network. Our RMLSA approach demonstrates an execution time of 2.81 \(\pm\) 0.47 ms in the context of 200 request assignments. In contrast, the First-Fit method exhibits an execution time of 1.17 \(\pm\) 0.16 ms, while the Random Wavelength Assignment method records an execution time of 0.8 \(\pm\) 0.22 ms. Notably, the RMLSA approach proves to be competitive compared to established methodologies in the state of the art. Furthermore, we are exploring hardware acceleration approaches to enhance the algorithm's performance. Figure 14: BBR histogram obtained in each network request for the FF (**a**) and RWA (**b**) algorithms.