Optical Communication Technology, Volume. 47, Issue 9, 36(2021)

Online bandwidth resources allocation algorithm for multi-tenancy PON based on deep reinforcement learning

JI Chenyang1... BI Meihua1, ZHOU Zhao2, CHEN Tianning1, LIN Jiaqian1 and XU Zhiwei1 |Show fewer author(s)
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    References(9)

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    [5] [5] SCHNEIR J R, XIONG Y. Cost analysis of network sharing in FTTH/PONs[J]. IEEE Communications Magazine, 2014, 52(8): 126-134.

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    [7] [7] LI C, GUO W, WANG W, et al. Bandwidth resource sharing on the XG-PON transmission convergence layer in a multi-operator scenario[J]. Journal of Optical Communications and Networking, 2016, 8(11): 835-843.

    [8] [8] MAO H, ALIZADEH M, MENACHE I, et al. Resource management with deep reinforcement learning[C]//15th ACM Workshop on Hot Topics in Networks, November 9-10, 2016, Atlanta, USA. New York: ACM, 2016: 50-56.

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    [10] [10] RAZA M R, NATALINO C, HLEN P, et al. A slice admission policy based on reinforcement learning for a 5G flexible RAN[C]//2018 European Conference on Optical Communication (ECOC), September 23-27, 2018, Roma, Italy. Roma: ECOC, 2018: 1-3.

    [11] [11] HINTON G. Overview of mini-batch gradient descent[EB/OL]. [2020-10-30]. http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_ lec6.pdf.

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    JI Chenyang, BI Meihua, ZHOU Zhao, CHEN Tianning, LIN Jiaqian, XU Zhiwei. Online bandwidth resources allocation algorithm for multi-tenancy PON based on deep reinforcement learning[J]. Optical Communication Technology, 2021, 47(9): 36

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    Paper Information

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    Received: Nov. 25, 2020

    Accepted: --

    Published Online: Nov. 15, 2021

    The Author Email:

    DOI:10.13921/j.cnki.issn1002-5561.2021.09.009

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