AI-Assisted Network-Slicing Based Next-Generation Wireless Networks

TLDR

Next‑generation wireless networks are highly heterogeneous and dynamic due to the integration of diverse radio access technologies, resource scales, and network functions, and emerging use cases such as machine‑to‑machine communication, autonomous driving, and factory automation impose stringent reliability, latency, and throughput requirements that challenge architecture design, network management, and resource orchestration. This paper aims to clarify the overall architecture of NGWNs and to identify three specific research problems within that architecture. The authors propose a network‑slicing architecture that incorporates artificial intelligence, and they detail the motivation, challenges, existing work, and future directions for applying AI to flexible RAN slicing, automated RAN technology selection, and mobile edge caching and content delivery. The study concludes that AI‑based approaches offer significant benefits and potentials for advancing NGWN research.

Abstract

The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based architecture and explain why and where artificial intelligence (AI) should be incorporated into this architecture. Second, the motivation, research challenges, existing works, and potential future directions related to applying AI-based approaches in three research problems are described in detail, i.e., flexible radio access network slicing, automated radio access technology selection, and mobile edge caching and content delivery. In summary, this paper highlights the benefits and potentials of AI-based approaches in the research of NGWNs.