Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features

Ali Al Housseini; Carlos Natalino; Paolo Monti; Omran Ayoub

eess.SPJul 24, 2026

Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features

Authors: Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub

Organizations: University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland · University of Southern Switzerland, Lugano, Switzerland · Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

Abstract

We propose a retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on source-domain-specific decision boundaries. The proposed approach supports both zero-shot and few-shot adaptation without requiring model retraining. Experimental results on cross-domain QoT datasets demonstrate improved generalization performance compared with conventional machine learning baselines and recent contrastive learning approaches, highlighting the potential of retrieval-based inference for robust optical network automation.

Explore similar work

Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning
Jul 22, 2026Ali Al Housseini, Carlos Natalino, Paolo Monti +1Cross-Domain Generalization Contrastive Learning
Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring
Apr 20, 2026Leyla Sadighi, Stefan Karlsson, Carlos Natalino +7Domain Adaptation Cross-Domain Generalization
OT on the Map: Quantifying Domain Shifts in Geographic Space
Apr 17, 2026Haoran Zhang, Livia Betti, Konstantin Klemmer +2Out-Of-Domain Generalization Cross-Domain Shifts

Jul 22, 2026 · cs.NIJ/K move · Enter open · S save

Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning

Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub

University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland · University of Southern Switzerland, Lugano, Switzerland · Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks. Models trained on data from a specific topology or operational configuration often exhibit degraded performance when deployed in unseen networks. In this work, we address this challenge by proposing a representation learning technique aimed at capturing task-relevant relationships that remain stable across domains. The proposed technique is based on a novel joint contrastive and classification learning approach in which representation learning and task optimization are performed simultaneously, allowing both objectives to shape the latent space. Experimental results on a representative use case, namely, lightpath quality of transmission estimation, demonstrate the effectiveness of our approach compared to baseline approaches, and highlight its capacity for rapid adaptation, providing excellent performance even with limited fine-tuning.

Cross-Domain Generalization Contrastive Learning Optical Networks Representation Learning Transmission Binary Classification

Open paper Ask about it PDF

Abstract

Explore similar work

Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning

Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring

OT on the Map: Quantifying Domain Shifts in Geographic Space