Radio Access Technology

Radio Access Technology (RAT) research focuses on optimizing the selection and management of multiple wireless technologies (e.g., 4G, 5G, Wi-Fi) to enhance network performance and user experience. Current research emphasizes the use of artificial intelligence, particularly reinforcement learning (including Deep Q-Learning and multi-agent variations) and federated meta-learning, to dynamically allocate resources and manage handovers between different RATs, often within heterogeneous networks. These advancements aim to improve key metrics such as latency, throughput, and energy efficiency, impacting both the design of future wireless networks and the development of new applications like vehicle-to-everything (V2X) communications.