Interference Detection in Spectrum-Blind Multi-User Optical Spectrum as a Service

TL;DR

This paper presents a machine learning approach for detecting and attributing interferences in multi-user optical spectrum sharing environments, achieving over 90% accuracy without requiring detailed user information.

Contribution

It introduces a novel ML-based architecture that identifies interference sources in OSaaS networks using only coarse measurements, enhancing network security and reliability.

Findings

Achieved 90.3% classification accuracy in interference detection.

Validated approach on a 190 km optical testbed with three users.

Demonstrated effectiveness without internal user spectrum data.

Abstract

With the growing demand for high-bandwidth, low-latency applications, Optical Spectrum as a Service (OSaaS) is of interest for flexible bandwidth allocation within Elastic Optical Networks (EONs) and Open Line Systems (OLS). While OSaaS facilitates transparent connectivity and resource sharing among users, it raises concerns over potential network vulnerabilities due to shared fiber access and inter-channel interference, such as fiber non-linearity and amplifier based crosstalk. These challenges are exacerbated in multi-user environments, complicating the identification and localization of service interferences. To reduce system disruptions and system repair costs, it is beneficial to detect and identify such interferences timely. Addressing these challenges, this paper introduces a Machine Learning (ML) based architecture for network operators to detect and attribute interferences to specific OSaaS users while blind to the users' internal spectrum details. Our methodology leverages available coarse power measurements and operator channel performance data, bypassing the need for internal user information of wide-band shared spectra. Experimental studies conducted on a 190 km optical line system in the Open Ireland testbed, with three OSaaS users demonstrate the model's capability to accurately classify the source of interferences, achieving a classification accuracy of 90.3%.