Sixth generation (6G) networks are expected to accommodate a significant increase in mobile broadband data traffic and to support diversified applications, such as remote healthcare, industrial automation, or autonomous driving. The emergence of new applications not only imposes stricter and more differentiated quality-of-service (QoS) requirements, such as ultra-high reliability, low latency, and detection accuracy, but also introduces the consumption of heterogeneous types of resources, such as communication, sensing, computing, and caching resources. Therefore, proper orchestration of multiple types of resources is required to achieve satisfactory QoS provisioning. To satisfy the differentiated QoS requirements, a series of advanced networking technologies, such as network slicing, digital twin, integrated sensing and communication, space-air-ground integrated networking, and software-defined networking (SDN) have emerged to enhance multi-dimensional resource utilization in a flexible and cost-effective way.

However, relying solely on mathematical modeling methods for resource orchestration, for example, stochastic analysis or optimization, is often computationally complex or even analytically intractable due to the increasingly large network scale, intensified data traffic volume, and uncertain network dynamics. Leveraging big data analytics and artificial intelligence (AI) techniques to learn resource orchestration decisions can be computationally effective, especially when the network environment becomes complex, dynamic, and uncertain. However, there remain many technical challenges on how to properly leverage and design customized AI methods to facilitate intelligent resource management in different 6G network scenarios for QoS provisioning, such as Industrial IoT, space-air-ground integrated networking, and autonomous vehicular networks.

The objective of this Special Issue is to provide a platform to attract and disseminate high-quality research findings and practical solutions from both academia and industry to advance AI-empowered resource orchestration for QoS provisioning in 6G.

Potential topics include but are not limited to the following:

• AI-based mobile edge computing/caching
• AI-based network slicing for QoS customization
• Integrated sensing and communication design
• AI-assisted integrated sensing and communication
• AI-assisted multi-dimensional resource orchestration
• Space-air-ground integrated networking for 6G
• SDN/ network function virtualization (NFV)-based resource management for 6G
• AI-assisted network slicing for vehicular networks
• Testbed design for AI-based resource management
• Intelligent pricing of resources for QoS guarantee
• Distributed AI for multi-resource orchestration
• Federated learning and split learning in 6G
• Edge-intelligence-assisted 6G networking
• Digital twin assisted networking design
• Holistic network virtualization design for 6G