# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
###### Abstract  
Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources. a 2024 13  
areas) is through network adoption by providing offshore services. The 5G Infrastructure Public-Private Partnership (5G PPP) introduced a vision to have an architecture design as a single access network comprising terrestrial networks (TNs) and non-terrestrial networks (NTNs) to advance ubiquitous connectivity [1]. An example of NTN technology is satellite technology. Satellite ubiquity aptitude demonstrates its presence for quick and accessible options for service provisioning. The advancement of technology towards 6G is encouraged by diverse use cases and supported by advanced transport networks. Nonetheless, there are missing areas in integrating the heterogeneity of use cases and transport networks that could lead to more efficient infrastructure. This study proposed an intelligent steering mechanism for regrouping the data that shared the same attributes and matched the transport network properties. The data or traffic in this study were reorganized and regrouped into a total number of available transport networks arranged in a particular service area. Each data group was redirected towards optimum-matched transport technologies compared to the conventional traffic-splitting method. For example, in an urban area where the current terrestrial transport network is reaching capacity, expansion works are not time- and operationally feasible; therefore, integrating a non-terrestrial network is the most viable alternative, and steering the traffic to alternative paths is a feasible approach. Therefore, the most viable alternative is integrating an NTN technology and redirecting the traffic to alternative paths. Hence, utilizing existing satellite and cable TV is an option in which these two technologies are dynamically provisioned and readily available [6; 7; 8]. This paper defines an optimization problem that aims to maximize overall resource utilization by steering pertinent traffic types to appropriate transport networks that resolve unnecessary issues such as traffic delays and congestion. The process assigned relevant traffic clusters to appropriate transport networks, using machine learning (ML) and mixed integer linear programming (MILP) techniques. Subsequently, the resource assignment method was manipulated to determine the optimal assignment between the cluster and the transport network. The objectives of this study are as follows:  
1. Introduce a traffic steering scheme that optimizes the coexistence of heterogeneous transport networks. 2. Demonstrate the comparative measures for traffic steering using ML and MILP against traffic classifications. 3. Demystify the factors contributing to the cluster-transport assignment. The remainder of this study is organized as follows: Section 2 refers to several research studies related to this study. Section 3 details the foreseeable issues discovered by these studies. Section 4 discusses the framework for the traffic-to-transport network assignment proposal.