Machine-Learning-Based Adaptive Wireless Network Selection for Terrestrial and Non-Terrestrial Networks in 5G and Beyond

Non-terrestrial networks (NTNs) have become increasingly crucial, particularly with the standardization of fifth-generation (5G) technology. In parallel, the rise of Internet of Things (IoT) technologies has amplified the need for human-centric solutions in 5G and beyond (5 GB) systems. To address diverse communication requirements from a human-centric perspective, leveraging the advantages of both terrestrial networks (TNs) and NTNs has emerged as a key focus for 5 GB communications. In this paper, a machine learning (ML)-based approach is proposed to facilitate decision making between TN and NTN networks within a multi-connectivity scenario, aiming to provide a human-centric solution. For this approach, a novel synthetic dataset is constructed using various sensing information, based on the assumption that numerous interconnected sensor systems will be available in smart city networks with sixth-generation (6G) technologies. The ML results are derived from this newly generated dataset. These simulation results demonstrate that the proposed approach, designed to meet the requirements of next-generation systems, can be effectively utilized with 6G.