Network slice reconfiguration by exploiting deep reinforcement learning with large action space

Wei, F., Feng, G., Sun, Y. , Wang, Y., Qin, S. and Liang, Y.-C. (2020) Network slice reconfiguration by exploiting deep reinforcement learning with large action space. IEEE Transactions on Network and Service Management, 17(4), pp. 2197-2211. (doi: 10.1109/TNSM.2020.3019248)

222926.pdf - Accepted Version



It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic traffic load, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by numerous variables. In this paper, we investigate the reconfiguration within a core network slice with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). This problem is also intractable by using conventional Deep Q Network (DQN), as it has a multi-dimensional discrete action space which is difficult to explore efficiently. To address the curse of dimensionality, we propose a discrete Branching Dueling Q-network (discrete BDQ) by incorporating the action branching architecture into DQN, for drastically decreasing the number of estimated actions. Based on the discrete BDQ network, we develop an intelligent network slice reconfiguration algorithm (INSRA). Extensive simulation experiments are conducted to evaluate the performance of INSRA and the numerical results reveal that INSRA can minimize the long-term resource consumption and achieve high resource efficiency compared with several benchmark algorithms.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Liang, Professor Ying-Chang and Feng, Professor Gang and Sun, Dr Yao
Authors: Wei, F., Feng, G., Sun, Y., Wang, Y., Qin, S., and Liang, Y.-C.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Network and Service Management
ISSN (Online):1932-4537
Published Online:25 August 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE Transactions on Network and Service Management 17(4): 2197-2211
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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