Traffic prediction for PON based on pre-matched cross-port transfer learning

Yu Wang; Shilong Mao; Zhichao Xiu; Jiaqi Xu; Yiqiang Hua

doi:10.1117/12.3023578

18 March 2024 Traffic prediction for PON based on pre-matched cross-port transfer learning

Yu Wang, Shilong Mao, Zhichao Xiu, Jiaqi Xu, Yiqiang Hua

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 131044D (2024) https://doi.org/10.1117/12.3023578
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

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.

Citation Download Citation

Yu Wang, Shilong Mao, Zhichao Xiu, Jiaqi Xu, and Yiqiang 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

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