# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 3 Network Architecture, Learning Framework, and Methodology  
The evolution of network generation from 4G to 5G and beyond enables new technologies to address the requirements of multiple domains from the industry. The aspiration for 6G continued to expand and provide extensive service coverage via feasible technologies with the adoption of non-terrestrial networks like satellite technology. Moreover, based on the niche characteristics of a specific locality, the footprint of such technologies is vast and reliable. Ref. [13] mentioned that the service coverage of Data Over Cable Service Interface Specification (DOCSIS) Cable TV services comprises 67% of total fixed broadband subscriptions. On the other hand, the satellite is the optimal candidate for \"Ubiquitous Services\" because of its innate capability to reach a place where standard terrestrial network technologies could not reach. Thus, the study opted to analyze a scenario of the co-existence of three different types of transport networks in overall network architecture, as shown in Figure 1. Then, the mechanism to enable the traffic steering function is formulated by adopting a sequential process of collecting raw data and performing data cleaning and transformation. Followed by the clustering of packets flowing to the access node, each computed cluster is assigned to the appropriate transport network that shares similar characteristics with packets in the respective clusters. The role of the AI plane in Figure 1 is to store the collected data from access and edge nodes, execute a series of machine learning algorithms, and create and store ML models. Based on the research work in [8], the parameters such as download (DL), upload (UL), delay, and error rate are collected from nodes. The aggregated packets then undergo unsupervised learning to extract shared attributes and clusters into three transport network selection options for traffic steering functions. The subsequent process is optimization, which uses the mixed integer linear programming (MILP) method to identify the optimum traffic-transport assignment for each cluster. The processes classify each packet using DL/UL, delay, and error rate parameters. Then, the processes label the traffic into the cluster and the assigned transport network type. The overall process is implemented offline, and each output is stored for classification learning to produce a model that represents the niche characteristics of traffic generated in a specific area. Ultimately, the classification model is envisioned to be used in a node where the traffic is labeled and steered to the appropriate transport network. The proposed concept adopts a similar approach of assigning a bearer using the Quality of Service Flow Index (QFI). The end-to-end algorithm infers and is deliberate from the study in [4]. The overall process is illustrated in Figure 2 with high-level information on input and output from such activities. The framework of this study is to explore the classification works using the data from the earlier works done in [8], represented by Steps 1 to 5, as elaborated below. Thus, the scope of supervised learning begins from Step 6 onwards. 1. Data collection: raw data generated from UE's traffic and transport network. 2. The data transformation process involves cleaning and transforming from multivariate to two-dimensional data using unsupervised learning, known as dimensional reduction. 3. Extraction of information: 1. UE's generated traffic undergoes a clustering process using an unsupervised learning technique to form a defined number of clusters with attribute information for each cluster. Figure 1: Heterogeneous transport network in 5G network architecture. Figure 2: The high-level organization flow. The extraction of attribute information for every type of transport network. * The preparation of clusters onto a transport matrix format for the subsequent matching process. The matching process runs matching algorithms to capture every pair of matching values based on clusters and transport attributes.