# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 1 Introduction  
Machine learning is obtaining an important role in many industries. The usability of machine learning contributes to regression, classification, clustering, dimensionality reduction, and decision-making tasks that constitute supervised learning, unsupervised learning, and reinforcement learning, respectively. Each machine learning is driven by distinctive functionalities such as predictive tasks, insight extraction from the unlabeled data, and the responses from the current state. The adoption of machine learning by industries, specifically in the telecommunication field, is focused on lowering operating costs, creating highly automated processes, creating new revenue streams, and enabling faster provisioning processes (fulfilment and assurance) [1]. Machine learning applications are positioned to accelerate a more critical use of resources to enable sixth generation (6G) networks [1]. This progression in the telecommunication industry incited the growth of many applications to carry specific tasks in various flows/activities from 5G, subsequently progressing to improve in similar ways for 6G. Traffic steering functions optimize the overall flows for traffic traversing in the telecommunication infrastructure. This function gives bare load balancing functionality to dynamic traffic management by re-routing the traffic based on network conditions [2]. Ultimately, the traffic steering function computes the end-to-endpath of the traffic flow to obtain the optimum route. Other than optimizing the flow, the study in [3] adopts the traffic steering method to improve the reliability of the data transmission utilizing heterogeneous access networks. This area is further investigated by [4; 5] to improve the overall resource using distinctive machine learning methods to predict traffic demands and avoid congestion. 6G aspires to have new use cases, more devices, and ubiquitous connectivity. The two essential elements qualifying the next generation of networks into 6G are the diverse use cases and the heterogeneous transport networks. According to the study in [6], four pillars support the 6G aspirations: (1) Enhanced Human Communication, (2) Enhanced Machine Communication, (3) Enabling Services, and (4) Network Evolution. In particular, this paper focuses on the network evolution of 6G, which aims to enable artificial intelligence (AI) and expand service ubiquity. AI and service ubiquity emerge as new opportunities to provide additional options for transport network resources to the overall service deliverables. The research expands to understand the approach to maximizing the enormous volume of data generated in the infrastructure, potentially giving valuable insight for subsequent actions to fulfil the service ubiquity aspiration by enabling the integration of a non-terrestrial network (NTN) into the terrestrial network (TN). The innate nature of satellite technology from NTN integration gives inevitable service coverage to an area. Hence, extending the TN to NTN creates new resource provisioning benefits. Nonetheless, incorporating the NTN into 5G and 6G is the main challenge while upholding the existing critical requirements in 5G and future 6G. The applications of machine learning in telecommunication processes are vast and diverse. However, machine learning essentially works with large volumes of data, and handling data for machine learning processes requires massive processing computations and more time. Incorporating a new process into 5G and 6G infrastructure using machine learning shall consider the time-critical applications because 6G demands a lower latency value [7]. Based on the study in [4; 5], an Open Radio Access Network (O-RAN) relies on non-real-time and near-real-time nodes to perform offline learning of the data and push the programmed inference to the RAN component. Although machine learning gives new propositions to the decision-making process, a comprehensive study is required to ensure the non-impairment of the latency value.