| dc.description.abstract | AI is vital in edge computing for 6G systems and the metaverse, optimizing resource management and enhancing user experiences by enabling real-time adaptation and efficient task distribution across local devices, edge servers, and the cloud. It ensures seamless connectivity, reduces latency, and improves overall system performance and scalability.The O-RAN architecture enhances mobile network flexibility and efficiency through open interfaces and AI/ML-driven automation for tasks like resource management and handover prediction, ensuring scalability and performance. Challenges include selecting optimal AI/ML models and deployment locations, crucial for maintaining low latency and effective resource allocation.Effective resource allocation in 5G-MEC focuses on minimizing latency and maximizing data rates to ensure high QoS, thereby improving user experience and system performance. Advanced techniques like deep reinforcement learning (DRL) are proposed to handle the dynamic and resource-intensive demands of 6G-enabled mobile edge computing (MEC) and Metaverse applications, optimizing resource allocation and reducing energy use and computation delays.We integrated Meta-Learning into DRL algorithms for better resource allocation in 5G MEC systems, enhancing adaptability and effectiveness. By adding an attention mechanism and a hierarchical structure for O-RAN, we significantly improved feedback, flexibility, and resource management, ensuring superior QoS for diverse applications.The Meta-Attention method consistently demonstrates superior performance, with an average satisfaction increase of 22\% compared to Meta-Learning approaches. | en_US |