# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 6 Discussion and Findings
### DL and UL Cluster-Transport Assignment Process Results  
This study clarified the matching process output between the clusters and transport network attributes in a vector value produced by the unsupervised dimensional reduction T-SNE method. A post-MILP process assigned a vector value of clusters to the transport types. The vector value was then reformed to the initial state, demonstrating the distinctive parameters of traffic size, time, and error rate. 2.1 The Performance of the Cluster-Transport Assignment in Scenario 1 (Dedicated Transport Network to a Single Node in a Service Area)  
Table 3 indicates the total DL and UL results, indicating the performances of the clustering process based on machine learning techniques and assignments. The parameters for the assignment performance included the traffic size (DL and UL), time, and error rate. Moreover, the compatible assignment of the cluster was based on the computed parameters (see Tables 1 and 2). Each cluster's performance is measured by the total number of clusters, complying with the performance matrix in Table 3. The highest scoring percentage indicates the highest number of compliances for every instance and cluster formed. In each cluster, total traffic (DL or UL) is summed and matched with the available link capacity. Thus, both DL and UL traffic are within the set resources. Nonetheless, the PDB and PER show  
Figure 8: The post-TSNE DL dataset of UEs for the clustering process. Figure 9: The post-TSNE UL dataset of UEs for the clustering process. low scores because of the maximum value of PDB captured from UEs to represent the cluster, though many other UEs have lower values than the RTT of the transport link. The inverse proposition involves PER because the minimum PER value generated represents the cluster. Each cluster was assigned to the appropriate transport type following the UL and DL parameters and transport type. Consequently, the values in each cluster against the designated transport network populating the high traffic usage were assigned to the optical network. This outcome was attributed to the high capacity of transport links (see Table 4). Table 4 indicates that at every instance, the maximum traffic size from every cluster formed is assigned to optical network because of the high-capacity link, whereas DOCSIS cable shows the adoption of handling 52% of the minimum traffic size captured from the clusters in each instance. 2.2 The Performance of the Cluster-Transport Assignment in Scenario 2 (A Shared Transport Network in an Area with Multiple Service Nodes)  
The following result showed a shared resources scenario that elucidated the selection mechanism behavior if the primary transport network (optical network) was shared and possessed low link capacity. Additionally, the cluster assignment to the transport network shifted by assigning high-traffic clusters with the highest capacity to the satellite transport network. This finding was similar to the output in Table 3, except that there are instances where the total traffic in DL and UL in the clusters computed higher than the available resources link, thus contributing to the two per cent drop in the score (see Table 5). The cluster assignment to the transport network revealed minor changes in the UL parameter. Notably, the UL capacity for DOCSIS cable was set to 6 Gbps per link instead of 10 Gbps of DL streams (see Table 1). The assignment process in shared transport networks followed a similar pattern (inclination towards high-capacity link). Thus, the net available capacity in the optical network was low, shifting the high traffic size to satellite networks and minimum DL values \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Traffic Type** & **Optical Network: TR1** & **Satellite Network: TR2** & **DOCSIS cable: TR3** \\ \hline
1 & Max DL & 77\% & 6\% & 8\% \\ \hline
2 & Min DL & 23\% & 25\% & 52\% \\ \hline
3 & Max UL & 85\% & 8\% & 7\% \\ \hline
4 & Min UL & 15\% & 50\% & 35\% \\ \hline \hline \end{tabular}
\end{table}