Auxiliary Neural Network Assisted Machine Learning for Extended Band Power Optimization

An Aux-CNN model-based input power estimation method for optical transmission systems was proposed, which is experimentally validated by an L-band WDM system, achieving an MAE of < 0.21dB with 20% of the training data.

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Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/acp/ipoc63121.2024.10810024
Primary Topic
Electromagnetic Compatibility and Noise Suppression
Type
article
Field-Weighted Citation Impact
0.00

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article

Auxiliary Neural Network Assisted Machine Learning for Extended Band Power Optimization

Tianqian Zhang, Yiwen Yang, Li Han, Ming Luo et al.
Electromagnetic Compatibility and Noise Suppression
article

Auxiliary Neural Network Assisted Machine Learning for Extended Band Power Optimization

Tianqian Zhang, Yiwen Yang, Li Han, Ming Luo, Wu Liu, Zhiyi Zhong
article en

Abstract

An Aux-CNN model-based input power estimation method for optical transmission systems was proposed, which is experimentally validated by an L-band WDM system, achieving an MAE of < 0.21dB with 20% of the training data.

National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 77%
Electromagnetic Compatibility and Noise Suppression
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Auxiliary Neural Network Assisted Machine Learning for Extended Band Power Optimization — Tianqian Zhang, Yiwen Yang, et al. · (2024) | TGRS Research Map | TGRS