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Add to EdgeExperimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks
Feb 11, 2026
We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency.
* Accepted at Optical Fiber Communications Conference 2026 (OFC 2026)
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Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission
Oct 11, 2022
A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity.
* 2 pages, 3 figures. Submitted to the OSA Advanced Photonics Congress
2021. Presented in Signal Processing in Photonic Communications (SPPCom)
2021. From the session: Neural Networks Applications for Photonic Systems
(SpM5C)
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