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.
Highlights
NEXT-GENERATION WIRELESS NETWORKSThe evolution of mobile communications from the first to the fifth generation (5G) has revolutionized many aspects of human society in the past four decades
Due to the challenges mentioned in the introduction, the next-generation wireless networks (NGWNs) architecture is expected to have the following prorpeFrtlieexsib[4le1]a:nd scalable, to support a wide range of service types and quality of service (QoS) requirements, and to support scalable r slice management after the deployment of slices; Automated and adaptive, to support automated radio access network (RAN) and cloud network resource allocation and adaptation based on data traffic and network performance, and to support
In this paper, we have illustrated the network-slicing based architecture, focusing on the RAN, and elaborated how artificial intelligence (AI) can potentially empower this architecture for NGWNs
Summary
The evolution of mobile communications from the first to the fifth generation (5G) has revolutionized many aspects of human society in the past four decades. Managing NGWNs requires the development of scalable and adaptive models and approaches that suit large-scale problems and heterogeneous network architectures, which should include both centralized and decentralized network control components. A major advantage of ML is its ability to handle complicated problems, which renders ML a powerful tool that suits the dynamic, heterogeneous, and decentralized features of NGWNs. Applying ML can potentially yield benefits such as improved performance and faster convergence in network management automation and performance optimization in large-scale systems. Applying ML in network slicing can provide the innovations required to address the aforementioned challenges in the network architecture and resource orchestration and, thereby, help fulfill the great prospect of NGWNs. The rest of this paper is organized as follows.
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