A learning algorithm for TWDM-PON’ DWBA with 5G fronthaul networks

Time and wavelength-division multiplexed passive optical network (TWDM-PON) is expected to be the fronthaul networks of 5G, which requires low latency and large bandwidth. In this paper, we propose a Q-learning based dynamic wavelength and bandwidth allocation (DWBA) algorithm for TWDM-PON. Simulati...

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Bibliographic Details
Published inOptical fiber technology Vol. 53; p. 102039
Main Authors Liang, Siyuan, Zhao, Feng, Jiang, Wenli
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.12.2019
Subjects
5G networks
Dynamic wavelength and bandwidth allocation
NG-PON2
Q-learning algorithm
TWDM-PON
NG-PON2
TWDM-PON
Dynamic wavelength and bandwidth allocation
5G networks
Q-learning algorithm
Online AccessGet full text
ISSN1068-5200
1095-9912
DOI10.1016/j.yofte.2019.102039

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Summary:Time and wavelength-division multiplexed passive optical network (TWDM-PON) is expected to be the fronthaul networks of 5G, which requires low latency and large bandwidth. In this paper, we propose a Q-learning based dynamic wavelength and bandwidth allocation (DWBA) algorithm for TWDM-PON. Simulation results show that the proposed DWBA algorithm can reduce the number of active channels by 40% and provide larger bandwidth with same wavelength number in comparation with the existing algorithms while satisfying the latency requirement of 5G fronthaul networks.
ISSN:1068-5200
1095-9912
DOI:10.1016/j.yofte.2019.102039