Distributed Learning Based Handoff Mechanism for Radio Access Network Slicing with Data Sharing

Sun, Y. , Feng, G., Zhang, L. , Valente Klaine, P. , Imran, M. A. and Liang, Y.-C. (2019) Distributed Learning Based Handoff Mechanism for Radio Access Network Slicing with Data Sharing. In: 53rd IEEE International Conference on Communications (IEEE ICC 2019), Shanghai, China, 20-24 May 2019, ISBN 9781538680889 (doi: 10.1109/ICC.2019.8761736)

Abstract

Network slicing (NS) has been identified as a fundamental technology for future mobile networks to meet extremely diverse communication requirements by providing tailored quality of service (QoS). However, due to the introduction of NS into radio access networks (RAN) forming a UE-BS-NS three-layer association, handoff becomes very complicated and cannot be resolved by conventional policies. In this paper, we propose a multi-agent reinforcement LEarning based Smart handoff policy with data Sharing, named LESS, to reduce handoff cost while maintaining user QoS requirements in RAN slicing. Considering the large action space introduced by multiple users and the data sparsity problem due to user mobility, LESS is designed to have two components: 1) LESS-DL, a modified distributed Q-learning algorithm with small action space to make handoff decisions; 2) LESS-DS, a data sharing mechanism using limited data to improve the accuracy of handoff decisions made by LESS-DL. The proposed LESS mechanism uses LESS-DL to choose both the target base station and NS when a handoff occurs, and then updates the Q-values of each user according to LESS-DS. Numerical results show that in typical scenarios, LESS can significantly reduce the handoff cost when compared with traditional handoff policies without learning.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Zhang, Professor Lei and Valente Klaine, Mr Paulo and Sun, Dr Yao and Feng, Professor Gang
• Authors: Sun, Y., Feng, G., Zhang, L., Valente Klaine, P., Imran, M. A., and Liang, Y.-C.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 1938-1883
• ISBN: 9781538680889
• Copyright Holders: Copyright © 2019 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304481		Resource Orchestration for Diverse Radio Systems	Lei Zhang	Engineering and Physical Sciences Research Council (EPSRC)	EP/S02476X/1	ENG - Systems Power & Energy