University of Oulu

X. Chen, Z. Zhao, C. Wu, T. Chen, H. Zhang and M. Bennis, "Secrecy Preserving in Stochastic Resource Orchestration for Multi-Tenancy Network Slicing," 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6, doi: 10.1109/GLOBECOM38437.2019.9013746

Secrecy preserving in stochastic resource orchestration for multi-tenancy network slicing

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Author: Chen, Xianfu1; Zhao, Zhifeng2; Wu, Celimuge3;
Organizations: 1VTT Technical Research Centre of Finland Ltd, Finland
2College of Information Science and Electronic Engineering, Zhejiang University, China
3Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan
4Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-06-04


Network slicing is a proposing technology to support diverse services from mobile users (MUs) over a common physical network infrastructure. In this paper, we consider radio access network (RAN)-only slicing, where the physical RAN is tailored to accommodate both computation and communication functionalities. Multiple service providers (SPs, i.e., multiple tenants) compete with each other to bid for a limited number of channels across the scheduling slots, aiming to provide their subscribed MUs the opportunities to access the RAN slices. An eavesdropper overhears data transmissions from the MUs. We model the interactions among the non-cooperative SPs as a stochastic game, in which the objective of a SP is to optimize its own expected long-term payoff performance. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game using the channel auction outcomes. Then we linearly decompose the per-SP Markov decision process to simplify the decision- makings and derive a deep reinforcement learning based scheme to approach the optimal abstract control policies. TensorFlow-based experiments verify that the proposed scheme outperforms the three baselines and yields the best performance in average utility per MU per scheduling slot.

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-7281-0962-6
ISBN Print: 978-1-7281-0963-3
Pages: 1 - 6
Article number: 9013746
DOI: 10.1109/GLOBECOM38437.2019.9013746
Host publication: 2019 IEEE Global Communications Conference, GLOBECOM 2019
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work carried out in this paper was supported by the Academy of Finland under Grants 319759, 319758 and 289611, the National Key R&D Program of China under Grant 2017YFB1301003, the National Natural Science Foundation of China under Grants 61701439 and 61731002, the Zhejiang Key Research and Development Plan under Grant 2019C01002, the JST-Mirai Program under Grant JPMJMI17B3, and 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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