Network Slicing

Network slicing dynamically partitions shared network infrastructure into isolated virtual networks, each tailored to specific applications' quality-of-service (QoS) requirements. Current research heavily utilizes machine learning, particularly reinforcement learning (with algorithms like DRL, PPO, and SARSA) and deep learning (including graph attention networks and neural networks), often coupled with digital twin modeling and federated learning, to optimize resource allocation and manage the complex interactions between slices. This approach aims to improve resource efficiency, ensure service-level agreements, and enhance security in 5G and beyond networks, impacting both network management practices and the development of novel AI-driven network control systems.