Artificial-Intelligence-Enabled Intelligent 6G Networks

TLDR

Rapid growth of smart terminals and diverse applications such as VR, remote surgery, and holographic projection has outpaced the capabilities of 4G and 5G, prompting industry and academia to pursue 6G networks that leverage artificial intelligence for enhanced performance. This article proposes an AI‑enabled intelligent architecture for 6G that enables knowledge discovery, smart resource management, automatic network adjustment, and intelligent service provisioning, and outlines future research directions in computation efficiency, algorithm robustness, hardware development, and energy management. The architecture comprises four layers—intelligent sensing, data mining and analytics, intelligent control, and smart application—and applies AI techniques to mobile edge computing, mobility and handover management, and spectrum management to optimize network performance.

Abstract

With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (AI) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an AI-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of AI techniques for 6G networks and elaborate how to employ the AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. Moreover, we highlight important future research directions and potential solutions for AI-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management.