Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks
Year: 2025
Type: preprint
Abstract: 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%.
Source: arXiv (Cornell University)
Language: English
Topic: Optical Network Technologies
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417155758