Access Control for RAN Slicing based on Federated Deep Reinforcement Learning

Liu, Y., Feng, G., Wang, J., Sun, Y. and Qin, S. (2021) Access Control for RAN Slicing based on Federated Deep Reinforcement Learning. In: 2021 IEEE International Conference on Communications (ICC 2021), 14-23 Jun 2021, ISBN 9781728171227 (doi: 10.1109/ICC42927.2021.9500611)

Abstract

Network Slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements sustainably. With the widespread of emerging smart devices, access control becomes an essential yet challenging issue in NS-based wireless networks due to the device-base station (BS)-NS three-layer association relationship. Meanwhile, stringent data security and device privacy concerns are increasing dramatically. In this paper, we propose an efficient access control scheme for radio access network (RAN) slicing by exploiting a federated deep reinforcement learning framework, called FDRL-AC, to improve network throughput and communication efficiency while enforcing the data security and device privacy. Specifically, we use deep reinforcement learning to train local model on devices, where horizontally federated learning (FL) is employed for parameter aggregation on BS, while vertically FL is employed for feature aggregation on the encrypted party. Numerical results show that the proposed FDRL-AC scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-art solutions.

• Item Type: Conference Proceedings
• Additional Information: This work has been supported by the National Key Research Project (Grant 2020YFB1806804), the National Natural Science Foundation of China (Grant 62071091), and ZTE Industry-Academia-Research Cooperation Founds.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao and Feng, Professor Gang
• Authors: Liu, Y., Feng, G., Wang, J., Sun, Y., and Qin, S.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISSN: 1938-1883
• ISBN: 9781728171227
• Published Online: 06 August 2021
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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