Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub
eess.SP cs.CV cs.IR cs.LG cs.NI
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.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.