AI-Native Network Slicing for 6G Networks

Wu, Wen; Li, Mushu; Wu, Huaqing; Zhou, Haibo; Zhang, Ning; Xuemin,; Shen,; Zhuang, Weihua

doi:10.1109/mwc.001.2100338

Cited by 168 publications

(54 citation statements)

References 13 publications

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“…Network slicing has been proposed to address the diversified service requirements [13]. The basic idea of network slicing is to create multiple virtual networks (i.e., slices) on top of a common physical network infrastructure to provide a flexible, centralised and programmable control and abstraction to the networks as well as eliminating the tight coupling between network functions and specific hardware units.…”

Section: Network Slicingmentioning

confidence: 99%

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

2022

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Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and edge computing, are envisioned as key enabling technologies. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. On the other hand, edge computing offers storage and computational resources at the edge of networks, hence providing real-time, high-bandwidth, low-latency access to radio network resources. As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential solutions with example use cases. In addition, this article further elaborated on the application of machine learning in edge-sliced networks and discussed some recent works as well as example deployment scenarios. Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed.

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Section: Network Slicingmentioning

confidence: 99%

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

2022

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“…To provide universal coverage, facilitate on-demand services, and support high-rate low-delay services. Space, Air, Ground, and Sea Integrated Networks (SAGSIN) will be exploited [58]. Wi-Fi, Bluetooth, THz, Ultra-WideBand (UWB), Visible Light Communications (VLC), biosensors, and satellite communication can all be integrated into 6G and should all come under the same standard so that they can all communicate with one another.…”

Section: Radio Access Technologies (Rats) In B5g/6gmentioning

confidence: 99%

“…Researchers highlight the need for AI-native network slicing (Slicing for AI, and AI for slicing) [58]. To handle large requests for dynamically constructing and updating E2E slices across multiple infrastructures, Reinforcement Learning (RL) approaches should be used to improve decision-making speed and accuracy [33].…”

Section: Network Slicing and Management In B5g/6gmentioning

confidence: 99%

5G shortcomings and Beyond-5G/6G requirements

Moussaoui

Bertin

Crespi

2022

2022 1st International Conference on 6G Networking (6GNet)

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Although 5G is designed to provide a large range of new digital services such as immersive experiences, connected machines, and Internet of Things (IoT), 5G networks may not be able to completely meet the needs of emerging novel applications, as well as to address the future needs of the business and regulatory ecosystem. Beyond 5G (B5G) and Sixth Generation (6G) wireless systems are expected to overcome 5G network limits and support a broader range of new use cases in this rapidly expanding digital era. In this survey article, we provide a summary of the major 5G networks shortcomings that are discussed in the literature and classify them into a taxonomy of six primary domains. For each of these domains, we also highlight and discuss the research challenges that will be faced in the development of future B5G and 6G networks.

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“…For instance, smart factory applications need to ensure a packet loss rate of less than 10 −9 and an end-to-end delay range of 250 𝜇s to 10 ms. Smart vehicular applications require a reliability level of more than 99.99% and an end-to-end delay of less than 1 ms [108]. Network slicing can effectively meet such diverse requirements of different Internet of intelligence applications by creating multiple logical network segments on a common shared physical infrastructure [109]. A multitenant environment is introduced in network slicing to support flexible provisioning of network resources and dynamic allocation of network functions [110].…”

Section: B Resources Virtualization Layermentioning

confidence: 99%

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Tang,

Yu,

Xie

et al. 2022

Preprint

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The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence.

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AI-Native Network Slicing for 6G Networks

Cited by 168 publications

References 13 publications

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

5G shortcomings and Beyond-5G/6G requirements

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

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