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Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks
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classification
cs.NI
keywords
usersimpairmentsosaasspectrumaccuracyaccuratelyachievingarchitecture
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We introduce a ML-based architecture for network operators to detect impairments from specific OSaaS users while blind to the users' internal spectrum details. Experimental studies with three OSaaS users demonstrate the model's capability to accurately classify the source of impairments, achieving classification accuracy of 94.2%.
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