# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 2 Related Works
effectively contributed to the concept of this study [6; 13]. [PERSON] et al. studied links connecting two distinctive sender and receiver nodes [14]. The study computed an optimum path based on various link attributes such as bandwidth, delay, and loss. Subsequently, the optimal link from the computing process decided to schedule the traffic to the link to carry the low latency and high data rate traffic. This study used a similar approach in which the selected transport network acquired specific bandwidth, delay, and error rate values. In contrast, the optimal matching attributes between the traffic and link determined the link or transport selection. An intelligent pre-path selection process was also necessary for the node, comprising various traffic types and terrestrial and transport network integration. This process divided the traffic into respective transport networks, and each transport network type exclusively handled similar traffic patterns within its domain. Although traffic engineering studies concerning path selections and routing decisions have existed for many years, the incoming technology can slightly modify the current states of 5G and 6G heterogeneous networks, routing strategy, and optimal path selection. Hence, integrating NTNs into the infrastructure is essential to present the network adoption benefits [15]. The coexistence of TNs and NTNs initiates the exploration of the mechanisms to redirect certain traffic types to designated transport networks [15]. Moreover, effectively directing traffic to various transport networks requires determining the optimal approach to distribute the traffic load across different transport network types. This necessity occursas each traffic possesses unique attributes that define its properties. For example, satellite constellations demonstrate quick resolution to infrastructure, service provisioning, and capacity planning constraints. Even though the satellite network is optimal for delivering efficient service coverage, the longer round-trip time constraint cannot be eliminated. Hence, intelligent mechanisms are vital in the infrastructure to comprehensively comprehend the traffic load properties and the transport network attributes. This process determines the most suitable traffic-transport allocation and assignment to optimize end-to-end efficiency. Several studies have investigated 6G networks across multiple layers. One approach is to pursue the capability to enable a disaggregated infrastructure and achieve an optimal operational state by implementing distributed service provisioning mechanisms [16]. Therefore, the end-to-end design of the next-generation network must be adapted to meet emerging demands. Numerous studies have implemented AI and traffic engineering methods to cope with the robust requirements supporting future networks. The advancement of ML facilitated the expansion of AI, which became one of its fundamental components. Furthermore, the innovation breakthrough in end-to-end operation and efficiency in the infrastructure has led to numerous enhancements. A study by [PERSON] et al. performed a thorough and chronological survey, analyzing many ML use case types in 5G infrastructure [17]. The study involved the elements at the network access, routing, congestion control, and application layers. Introducing newer use cases expands the traffic classifications for 6G networks. A study by [PERSON] et al. developed a distributed control mechanism using evolution game theory [16]. This mechanism resolved the traffic flow assignment issues from the UE. Hence, future network generation anticipates a highly dynamic state of traffic behavior. The pursuit of minimizing costs and optimizing resources are fundamental principles for ensuring the sustainability of operators or service providers. Likewise, a study by [PERSON] et al. emphasized a survey principle and listed the financial metrics that led to operators adopting resource-sharing mechanisms [18].