Device association for RAN slicing based on hybrid federated deep reinforcement learning

Liu, Y., Feng, G., Sun, Y. , Qin, S. and Liang, Y.-C. (2020) Device association for RAN slicing based on hybrid federated deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(12), pp. 15731-15745. (doi: 10.1109/TVT.2020.3033035)

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Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency incomparison with some state-of-the-art solutions.

Item Type:Articles
Additional Information:This work was supported by Development Program in Key Areas of Guangdong Province under Grant 2018B010114001, and the National Science Foundation of China under Grant 62071091.
Glasgow Author(s) Enlighten ID:Liang, Professor Ying-Chang and Feng, Professor Gang and Sun, Dr Yao
Authors: Liu, Y., Feng, G., Sun, Y., Qin, S., and Liang, Y.-C.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Vehicular Technology
ISSN (Online):1939-9359
Published Online:22 October 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE Transactions on Vehicular Technology 69(12): 15731-15745
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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