# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 2 Related Works
The diversity of use cases led to the translation to traffic classification, followed by the QoS Flow Index (QFI) method setting up the specific treatment of each packet in the infrastructure. From another perspective, the generated traffic from every UE has a range of characteristics set as a type of use case, which is made based on size and critical properties like latency and error rate. Nevertheless, certain applications (single-use cases) have attributes overlapping with other use cases. For example, virtual reality (VR) is classified as enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) (subject to operator definition). In some scenarios, a casual internet user produces low and non-critical traffic sharing attributes similar to a massive machine type communication (mMTC) use case traffic. These circumstances forage the investigation to demystify the interrelation between user equipment (UEs) in the serving area; hence, this study opted to manipulate the unsupervised machine learning to produce a cluster within it and find the classifications amongst the UEs that shared similar characteristics. A study by [PERSON] documented a comprehensive list of prospective use cases following the introduction of 6G networks [19]. The study revealed multiple novel use cases, such as holographic communication, extended reality (XR), tactile internet, and haptic communication [20; 21]. These use cases highlighted the extreme infrastructure requirements to deliver the products [20]. Figure 1 depicts the novel use cases for 6G shared multi-attributes. The hologram and XR use cases were effectively performed under low latency and substantial bandwidth provisions. Meanwhile, tactile internet demonstrated high reliability, security, and availability while occasionally requiring high bandwidth capacity [19]. Tactile internet also required the cross attributes of capacity, reliability, and low latency connectivity. Notably, [PERSON] examined and classified the 6G use cases by identifying the overlapping attributes shared by three 5G use cases [19]. Typically, the current system for splitting traffic employs various approaches, such as the round-robin scheduling method and a split ratio method that proportions traffic to available path options. A study by [PERSON] et al. applied a multipath scheduler to multiple transport links by classifying traffic types based on low- and high-latency traffic [14]. The study did not discover any relevant scheduling or splitting traffic mechanism research based on the attributes of specific transport network technology mentioned earlier. An alternative method proposed by [PERSON] et al. involved splitting the traffic into designated satellite and terrestrial paths based on both link conditions [6]. This method did not consider the assigned traffic type to the transport. Nonetheless, traffic and transport network conditions require optimal resource management in actual network conditions. Infrastructure-related resource management efficiency and network performance can be increased by directing pertinent traffic to appropriate links, specifically in highly densely populated traffic. Hence, more comprehensive splitting mechanisms are essential to comprehend and assign traffic intelligently to the transport network. New challenges are observed because various services and use cases require the most efficient operating infrastructure. A study by [PERSON] demonstrated that traffic engineering leveraged the adaptiveness of a network to traffic behavior [22]. The study classified the traffic types into four categories, representing real traffic observation from real networks. In addition, the study mitigated the bandwidth-blocking probability and implemented an optimization mechanism to resolve the routing and channel resource assignment. This algorithm computed the resources of a total number of light paths in one of the connecting nodes in the network and measured the channel usage size.