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Creators/Authors contains: "Mo, Weiyang"

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  1. ; ; ; ; (, OSA 2018)
    Abstract: An LSTM network is developed to predict BBU pool traffic in 5G C-RAN ROADM networks. 5G throughput improvement and resource savings are observed with resource reallocation by reconfiguring the optical network 30 minutes in advance. OCIS codes: (060.4256) Networks, network optimization; (060.0060) Fiber optics and optical communications 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, Optical Fiber Communication Conference)
    Full Text Available 
  3. ; ; ; ; (, Optical Fiber Conference)
    An LSTM network is developed to predict BBU pool traffic in 5G C-RAN ROADM networks. 5G throughput improvement and resource savings are observed with resource reallocation by reconfiguring the optical network 30 minutes in advance. OCIS codes: (060.4256) Networks, network optimization; (060.0060) Fiber optics and optical communications 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; (, 2018 European Conference on Optical Communication)
    Machine learning based modelling of Erbium-Doped Fiber Amplifiers (EDFA) is used to determine wavelength dependent gain for use in optical transmission systems, and achieves root mean square error (RMSE) of 0.08, 0.18, and 0.27 dB under input ranges of +/- 3, 6, 9 dB. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; (, Journal of optical communications and networking)
    Recent advances in software and hardware greatly improve the multi-layer control and management of reconfigurable optical add-drop multiplexer (ROADM) systems facilitating wavelength switching. However, ensuring stable performance and reliable quality of transmission (QoT) remain difficult problems for dynamic operation. Optical power dynamics that arise from a variety of physical effects in the amplifiers and transmission fiber complicate the control and performance predictions in these systems.We present a deep-neural-network-based machine learning method to predict the power dynamics of a 90-channel ROADM system from data collection and training. We further show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; (, Journal of Optical Communications and Networking)
  7. ; ; ; ; ; (, ACM Big-DAMA'17, Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks)
    Full Text Available