The rapid development of 5G communication networks and beyond (B5G) technologies bring an increasing amount of information transmitted over the Internet and stored in the local servers, which makes information exchange faster and more convenient. Meanwhile, the developed technologies have greatly enriched the application scenarios. This includes smart homes, UAV networks, electric autopilot, automatic driving in Internet of Vehicles and control systems, which require a large number of connections, higher transmission reliability, and less computation and communication latency. In addition, the interference introduced by massive connections will limit the system performance and reduce the efficiency of the traditional computing algorithms.

Consequently, efficient massive communication and computing is a challenging task in academia and industry. To address this issue, some advanced physical-layer technologies such as massive MIMO and intelligent reflecting surfaces should be studied, helping to increase the system resource dimension in the aspect of space. Moreover, some intelligent algorithms should be developed for future computing systems, such as deep learning, deep reinforcement learning, federated learning, and transfer learning. Furthermore, some advanced or intelligent resource management methods should be developed to enhance the system performance, which can fully exploit the potential advantages of the resources from communication and computing.

Motivated by these challenges, this Special Issue aims to disseminate the latest theoretical and experimental works in the domain of massive communication and intelligent computing technologies towards enabling massively connected, fully intelligent, and sustainable green 6G networks. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Massive MIMO design from the aspect of channel estimation, user detection, performance analysis, and resource management
• Machine learning-based technologies for the physical-layer communication
• Deep learning-based technologies for the mobile edge computing networks
• Intelligent resource management for massive communication and computing systems
• Cache assisted communication and computing systems
• Machine learning-enabled security techniques
• Energy and spectral efficient access technologies for B5G wireless communication networks
• Reconfigurable intelligent surfaces for B5G wireless communication networks
• Backscatter communication for B5G wireless communication networks
• Smart energy management techniques for balancing energy demand-supply in B5G wireless communication networks
• Analytical, optimization, and experimental approaches for green communications and computing in B5G wireless communication networks