# Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2503.17079 · HTML: https://mlsanity.com/paper/2503.17079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum](https://mlsanity.com/paper/2507.21728/) (2025)

## Abstract (arXiv)

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%.