Radio Access Network

Radio Access Networks (RANs) are the crucial communication infrastructure connecting mobile devices to the core network, and current research focuses heavily on improving their efficiency, intelligence, and adaptability. This involves leveraging machine learning, particularly reinforcement learning and deep learning models like transformers and graph neural networks, for tasks such as resource allocation, spectrum sharing, and predictive maintenance within open and virtualized RAN architectures like O-RAN. These advancements aim to optimize network performance, reduce energy consumption, and enable the seamless integration of diverse services, ultimately impacting both the scientific understanding of network optimization and the practical deployment of next-generation cellular networks.