# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 6 Conclusions and Future Works  
This study successfully constructed an intelligent mechanism to comprehend traffic load while assigning it to suitable transport networks. Unsupervised ML techniques were used to retrieve the optimal traffic cluster configurations. The result from clustering works shows the formation of cluster groups based on the density plots of the data. The cluster was then translated to numerical values representing the size of the traffic, delay, and packet loss. The cluster's numerical result was then relayed to the decision-making process to determine the optimum match to transport network numerical values. An intelligent mechanism was effectively demonstrated in choosing the appropriate transport type based on the traffic and transport attributes. This observation was shown from the movement of cluster assignment on transport network type demonstrated in scenarios 1 and 2. Therefore, an intelligent mechanism using ML techniques and solver-based solutions was successfully introduced and demonstrated to achieve a high percentage (98%) of dynamic traffic steering capability based on resource links.  
The decision-making process was assessed in this study by performing transport selection based on the standard use cases (scenario 2) defined in 5G (eMBB, mMTC, and URLLC). Thus, the UE clustering process (ML and use cases) observed a marginal performance difference with the same dataset. Moreover, using ML approaches in clustering yielded more definitive assignment outcomes for the selected transport network type. Nonetheless, this study identified the necessity for additional functionalities in traffic-transport assignment throughout the decision-making stage. Even though the assignment performed the selection based on the similarity attributes between the two sides, the UE traffic size and transport network capacity strongly influenced the assignment. Time and error rate attributes were not fully understood and demystified.  
Future studies should determine newer approaches to reconfigure the entire process and replicate the cluster configuration. This process requires repeating the decision-making stages to guarantee an indisputable assignment of traffic-transport type and produce a highly efficient assignment. Consequently, a finer approach shall be explored in depth, considering parameters such as time and error rate to produce more influential decisions on the assignment process.  
Conceptualization, H.A.H.N. and T.M.; methodology, H.A.H.N.; validation, H.A.H.N. and T.M.; formal analysis, H.A.H.N.; investigation, H.A.H.N.; resources, H.A.H.N.; data curation, H.A.H.N.; writing--original draft preparation, H.A.H.N.; writing--review and editing, H.A.H.N.; visualization, T.M.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest, and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.