Intelligent resource scheduling for 5G radio access network slicing

Yan, M., Feng, G., Zhou, J., Sun, Y. and Liang, Y.-C. (2019) Intelligent resource scheduling for 5G radio access network slicing. IEEE Transactions on Vehicular Technology, 68(8), pp. 7691-7703. (doi: 10.1109/TVT.2019.2922668)

212845.pdf - Accepted Version



It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms.

Item Type:Articles
Additional Information:This work was supported by the National Science Foundation of China under Grant number 61631005, and the Research and Development Program in Key Areas of Guangdong Province under Grant number 2018B010114001.
Glasgow Author(s) Enlighten ID:Liang, Professor Ying-Chang and Feng, Professor Gang and Sun, Dr Yao
Authors: Yan, M., Feng, G., Zhou, J., Sun, Y., and Liang, Y.-C.
College/School:College of Science and Engineering > School of Engineering
Journal Name:IEEE Transactions on Vehicular Technology
ISSN (Online):1939-9359
First Published:First published in IEEE Transactions on Vehicular Technology 68(8):7691-7703
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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