With the rapid development of mobile information technology and the increasing popularity of mobile devices, such as smart phones, mobile laptops, and tablets, more and more complex and computation-intensive mobile applications have become available. Due to the limited computing resources and battery capacity of mobile devices, it is very difficult for mobile devices to process these complex applications by themselves locally, however, the emerging paradigm of mobile edge computing can help address this issue. Mobile edge computing provides computing service at the network edge and can be deployed at the radio access point, such as a base station, reducing core network traffic and service latency. In addition, next-generation networks (6G) provide high transmission rates and low-latency services for mobile users, and so by integrating 6G and mobile edge computing techniques, 6G-enabled mobile edge computing has attracted tremendous attention from both academia and industry.

In 6G-enabled mobile edge computing, a careful and well-designed resource provision and orchestration strategy is crucial in providing mobile users with good quality of experience (QoE) in a cost-effective manner. However, there are several challenges. Firstly, the quality and state of the wireless channel is dynamic and varying over time and can be affected by not only the location of mobile devices, but also other factors like network congestion or fading. Moreover, the time-varying channel state is hard to accurately predict in practice, and obtaining the data and task generation process of each mobile device is also challenging. Secondly, as the numbers of mobile devices and mobile applications are rapidly increasing, the scale of resource provision and service orchestration problem has grown exponentially, resulting in a state space explosion. Therefore, it is vital to design efficient, low-complexity strategies. Artificial Intelligence (AI) techniques are practical solutions to address the above issues. AI can help process the data and tasks of mobile devices in real-time and make the corresponding resource provision and service orchestration decisions in a cost-effective and efficient manner. As such, AI-enabled resource provision and service orchestration has drawn extensive attention from both industry and academia.

This Special Issue aims to solicit high-quality contributions that share and discuss recent advances and future trends in AI-empowered resource provision and service orchestration for 6G-enabled mobile edge computing. Papers on theoretical research and industry applications are both invited, and we welcome both original research and review articles.

Potential topics include but are not limited to the following:

• AI-empowered resource reservation for 6G-enabled mobile edge computing
• AI-empowered content caching in 6G-enabled mobile edge computing
• AI for service orchestration and scheduling in 6G-enabled mobile edge computing
• AI for pricing and bidding of resources in 6G-enabled mobile edge computing
• Distributed AI for data placement and migration in 6G-enabled mobile edge computing
• Edge intelligence for big data analysis in 6G-enabled mobile edge computing
• Testbed of AI algorithms for task scheduling in mobile edge computing
• Industrial applications, data and platforms for 6G-enabled mobile edge computing
• Edge intelligence for QoE optimization of mobile users in mobile edge computing
• Intelligent service mashup for 6G-enabled mobile edge computing
• AI for privacy protection in 6G-enabled mobile edge computing
• Intelligent mobility management for service orchestration in mobile edge computing