Using Full-Dimensional Programmability to Power Self-Driving 6G Networks
Wu, Tong Yao, Haipeng Mai, Tianle Wang, Zunliang Wang, Fu Guizani, Mohsen
Wu, Tong
Yao, Haipeng
Mai, Tianle
Wang, Zunliang
Wang, Fu
Guizani, Mohsen
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Department
Machine Learning
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Journal article
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2025
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English
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Abstract
Recently, 6G has attracted widespread attention from both academia and industry. 6G networks are expected to exhibit even more heterogeneity than 5G networks, and support various emerging scenarios and applications such as virtual and augmented reality (VR/AR), air/ space/ground networks, and Internet of Things. Such massive heterogeneous devices pose huge challenges for network control and management. Recently, advances in artificial intelligence have brought a new clan of networks, termed as self-driving 6G networks. It utilizes network telemetry, artificial intelligence, and DevOps to simplify networks and operations, helping network owners improve network quality, as well as increase efficiency. However, the current network is built on closed merchant switching ASICs and a homegrown management and control system. Self-driving an opaque system without really knowing and controlling what they do is a hazardous and fruitless endeavor. Fortunately, the network programmability technology opens the possibility for running self-driving algorithms over the whole network. In this paper, we design a full-dimensional programmability empowered self-driving 6G network architecture. We discuss how network programmability can promote the release of network intelligence, from the view of both verticality (control and data plane) and horizontality (end to end). Moreover, three use cases are designed to demonstrate that the proposed architecture can automatically cope with the dynamically changing complex network environment, realize automatic perception and automatic decision, and then facilitate the automation level of the network and enhance the performance of the network.
Citation
T. Wu, H. Yao, T. Mai, Z. Wang, F. Wang and M. Guizani, "Using Full-Dimensional Programmability to Power Self-Driving 6G Networks," in IEEE Network, vol. 39, no. 1, pp. 270-277, Jan. 2025, doi: 10.1109/MNET.2024.3448288.
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IEEE Network
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IEEE