Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
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.
| Authors | Ali Al Housseini, Carlos Natalino, Paolo Monti et al. |
|---|---|
| Primary category | eess.SP |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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