# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 2 Related Works  
The future of the 6G transport network anticipates the possibility of multiple coexisting transport network technologies. Meanwhile, the 5G network optimally utilizes an optical transport network to connect the network elements. Given that the developed optical transport networks have made significant progress in facilitating various use cases, the optical network is extensively used for hyperscale applications in data center network architecture. This process adheres to the strict requirements of Data Center Interconnects (DCIs) [9]. A study by [PERSON] et al. reported a state-of-the-art passive optical network (PON) technology (NG-PON2) [10]. The study effectively performed the convergence process in a single PON infrastructure. Conversely, [PERSON] et al. described that the optical network deployment involved significant concerns regarding land acquisition and long deployment time [11]. There are circumstances where, geographically, it would not be optimal to lay the fiber networks in greenfield or brownfield areas. Hence, the most practical solution to address the increasing service demand is redirecting the traffic to alternative transport network technology (non-terrestrial network options). The software-defined network (SDN) and network function virtualization (NFV) technology also encourages software function developments in a white box, providing significant advantages to distributed nodes with steering (or load balance) and gateway functions to other transport network technologies. A study by [PERSON] et al. denoted the recent works in hybrid satellite and terrestrial fields, which provided twofold values [12] concerning the workability of the satellite transport network, and also revealed the issues concerning the necessary coordination steps in understanding the respective transport network technology properties and synthesizing the two technologies. These circumstances can inevitably result in the coexistence of heterogeneous transport networks, requiring an effective intelligent mechanism. Therefore, the coverage capability of satellite technology renders it a feasible option for transport network service accessibility. The Data Over Cable Service Interface Specification (DOCSIS) cable infrastructure is another technology option with an extensive footprint in residential areas [5]. Consequently, this study maximized the three coexistence lists of transport network technology by harnessing the ML ability and the utilization of an optimization solver to yield intelligent mechanisms. Another study by [PERSON] et al. surveyed the findings from an integration process involving TN and NTN transport technologies [12]. The study highlighted several challenges in adopting satellite technology, including resource management, the mechanism to offload traffic, and path selection or routing. Considering the satellite network attributes and the obligation to sustain traffic QoS, steering the right traffic to the right transport network was the underlying factor in these challenges. Each stipulated challenge was elaborated by establishing a mechanism for utilizing hybrid networks (satellite and terrestrial) to achieve the most effective configuration. The study also reported that using the traditional approach to handle traffic in the hybrid network or adopting a distinctive resource management approach from these two networks could generate inefficient resource utilization and compromise network performance. Therefore, future network generation should emphasize improved connectivity, including various technologies to support the exponential growth of data, devices, and use cases [1; 5; 7]. A novel mechanism is needed to maximize and achieve optimal integration in a heterogeneous network infrastructure due to the demands concerning future ideas and the unique attributes of integrating multiple transport network technologies. Numerous studies have investigated this condition in single-domain and multi-domain transport technologies. Two studies by [PERSON] et al. and [PERSON] et al.