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Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In this paper, we propose a self-sustained RAN slicing framework, which integrates the self-management of network resources with multiple granularities, the self-optimization of slicing control performance, and self-learning together to achieve an adaptive control strategy under unforeseen network conditions. The proposed RAN slicing framework is hierarchically structured, which decomposes the RAN slicing control into three levels, i.e., network-level slicing, next generation NodeB (gNodeB)-level slicing, and packet scheduling level slicing. At the network level, network resources are assigned to each gNodeB at a large timescale with coarse resource granularity. At the gNodeB-level, each gNodeB adjusts the configuration of each slice in the cell at the large timescale. At the packet scheduling level, each gNodeB allocates radio resource allocation among users in each network slice at a small timescale. Furthermore, we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control. With the proposed RAN slicing framework, the QoS performance of emerging services is expected to be dramatically enhanced.


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An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks

Show Author's information Jie MeiXianbin Wang*( )Kan Zheng
Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
Intelligent Computing and Communication (IC2) Lab, Wireless Signal Processing and Network (WSPN) Lab, Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China

Abstract

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In this paper, we propose a self-sustained RAN slicing framework, which integrates the self-management of network resources with multiple granularities, the self-optimization of slicing control performance, and self-learning together to achieve an adaptive control strategy under unforeseen network conditions. The proposed RAN slicing framework is hierarchically structured, which decomposes the RAN slicing control into three levels, i.e., network-level slicing, next generation NodeB (gNodeB)-level slicing, and packet scheduling level slicing. At the network level, network resources are assigned to each gNodeB at a large timescale with coarse resource granularity. At the gNodeB-level, each gNodeB adjusts the configuration of each slice in the cell at the large timescale. At the packet scheduling level, each gNodeB allocates radio resource allocation among users in each network slice at a small timescale. Furthermore, we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control. With the proposed RAN slicing framework, the QoS performance of emerging services is expected to be dramatically enhanced.

Keywords: Radio Access Network (RAN), network slicing, network resource management, intelligent network

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Publication history

Received: 30 July 2020
Revised: 31 October 2020
Accepted: 28 November 2020
Published: 30 December 2020
Issue date: December 2020

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© All articles included in the journal are copyrighted to the ITU and TUP 2020

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© All articles included in the journal are copyrighted to the ITU and TUP. This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/.

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