University of Oulu

X. Chen et al., "Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2377-2392, Oct. 2019,

Multi-tenant cross-slice resource orchestration: a deep reinforcement learning approach

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Author: Chen, Xianfu1; Zhao, Zhifeng2,3; Wu, Celimuge4;
Organizations: 1VTT Technical Research Centre of Finland, 90570 Oulu, Finland
2Zhejiang Lab, Hangzhou 310000, China
3College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
4Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan
5Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
6Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064 USA
7Information Systems Architecture Research Division, National Institute of Informatics, Tokyo 101-8430, Japan
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-05-04


With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

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Series: IEEE journal on selected areas in communications
ISSN: 0733-8716
ISSN-E: 1558-0008
ISSN-L: 0733-8716
Volume: 37
Issue: 10; SI
Pages: 2377 - 2392
DOI: 10.1109/JSAC.2019.2933893
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Academy of Finland under Grant 319759, Grant 319758, and Grant 289611, in part by the National Key R&D Program of China under Grant 2017YFB1301003, in part by the National Natural Science Foundation of China under Grant 61701439 and Grant 61731002, in part by the Zhejiang Key Research and Development Plan under Grant 2019C01002, in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant 18KK0279, and in part by the Telecommunications Advanced Foundation.
Academy of Finland Grant Number: 319759
Detailed Information: 319759 (Academy of Finland Funding decision)
319758 (Academy of Finland Funding decision)
289611 (Academy of Finland Funding decision)
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