Radio access network

Wireless network

Automation

Distributed computing

Architecture

Engineering

World Wide Web

Visual arts

Network architecture

Orchestration

Mobile station

Slicing

Computer network

Computer science

Wireless

Musical

Access network

Base station

Telecommunications

Mechanical engineering

IEEE Open Journal of Vehicular Technology

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.

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