Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning

Christian Dorize; Haik Mardoyan; Henrique Pavani; Jeremie Renaudier; Khouloud Abdelli; Patricia Layec; Sterenn Guerrier

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Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning

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arxiv 2409.03667 v1 pith:OCCCO4VS submitted 2024-09-05 cs.LG eess.SP

Khouloud Abdelli , Henrique Pavani , Christian Dorize , Sterenn Guerrier , Haik Mardoyan , Patricia Layec , Jeremie Renaudier This is my paper · ORCID

classification cs.LGeess.SP

keywords classificationlearningdatamachinenetworkopticalaccuracyacross

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We demonstrate mechanical threats classification including jackhammers and excavators, leveraging wavelet transform of MIMO-DFS output data across a 57-km operational network link. Our machine learning framework incorporates transfer learning and shows 93% classification accuracy from field data, with benefits for optical network supervision.

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