Multiaccess Edge Computing

Multiaccess Edge Computing (MEC) aims to bring computation and storage closer to users, reducing latency and bandwidth demands for applications requiring real-time processing. Current research focuses on optimizing resource allocation across multiple users and edge servers, often employing reinforcement learning algorithms like DDPG and actor-critic methods, sometimes enhanced with graph convolutional networks to model user interactions. These advancements are crucial for enabling demanding applications like the metaverse and industrial digital twins, improving performance and efficiency in diverse sectors.