PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
Based on the long short-term memory (LSTM) network, a temporal model of matching cross-port Transfer Learning (MCPT-LSTM) for predicting the traffic of PON is proposed and numerically studied, in which transfer learning is used for transferring a pre-established model of a source PON to a target one, effectively mitigating the challenges associated with inadequate data that typically hampers the accuracy of network traffic predictions within PON systems. Experimental evaluations employing operational PON network data underscore that the proposed model enhances the precision of traffic forecasting by a margin exceeding 20%.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yu Wang,Shilong Mao,Zhichao Xiu,Jiaqi Xu, andYiqiang Hua
"Traffic prediction for PON based on pre-matched cross-port transfer learning", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 131044D (18 March 2024); https://doi.org/10.1117/12.3023578
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.