Artificial-Intelligence-Enabled Intelligent 6G Networks

By Helin Yang, Arokiaswami Alphones, Zehui Xiong, Dusist Niyato, Jun Zhao, Kaishun Wu. IEEE Network, 2019

438 citations recorded by the source corpus.

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. 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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4bfdc3910684396e22f6675470002ef162bebdbf

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Citations in the corpus

Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends (2022, methodology): “3) Resource Allocation by Machine Learning: Apart from optimization based resource allocation designs, machine learning methods have also been used to optimize the power allocation strategies for given precoders [136].”
Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges (2021, background): “All in all, it is worth noticing the 5G NR development cycle is now mature, and the research interest is slowly shifting towards the potential 6G technologies and systems, as evidenced by the latest papers, which focus mostly on the communication aspects [2], [17]–[23].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “As to candidate techniques, commonly mentioned examples include the integration of satellite, aerial, terrestrial, and underwater networks [8], [9], (sub)terahertz and visible light communications [10], artiﬁcial intelligence (AI) empowered networks [11]–[13], to name a few.”
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “In addition, the research reveals that the node mobility is highly challenging in different environments (such as deep water ) [176].”
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges (2021, background): “DRL) will be top approaches [4], [218], [219], given their strength in learning from very large inputs and automatically optimizing the decisions based on continuous feedback from”