With the exploding increased mobile traffic data and unprecedented demands of computing capabilities, it is very challenging to execute increasingly complex applications in resource-constrained mobile devices. Future wireless communications are very complex with different radio access technologies, transmission backhauls, and network slices. More intelligent technologies are required to address such complicated scenarios and adapt to dynamic mobile environments.

Deep learning has already demonstrated overwhelming advantages in many areas. Recently, it has attracted much attention in the field of wireless communications and mobile computing. Deep learning driven algorithms and models can facilitate wireless network analysis and resource management and be of benefit in coping with the growth in volumes of communication and computation for emerging mobile applications. However, how to customize deep learning techniques for heterogeneous mobile environments is still under discussion. Learning algorithms in mobile wireless systems are immature and inefficient. More endeavors are needed to bridge the gap between deep learning and wireless communications and mobile computing research. This special issue solicits theoretical contributions and practical research related to the new technologies, analysis, and applications with the help of artificial intelligence and deep learning.

Potential topics include but are not limited to the following:

• New theories, scenarios, and applications of deep learning in wireless communications and mobile computing
• Design, development, and application of new deep learning algorithms in wireless communications and mobile computing
• Deep learning driven wireless sensor networks, cellular networks, Wi-Fi networks, and emerging mobile networks
• Deep learning driven wireless data analysis and mobility analysis
• Deep learning driven network security, network control, and network switching
• Deep learning driven heterogeneous wireless network architectures, mobile applications, and mobile systems
• Deep learning driven computation offloading and data offloading
• Deep learning driven mobile cloud computing (MCC), mobile edge computing (MEC), or fog computing (FC)