aiqbench analysis
This research introduces a retrieval-based framework that enables zero-shot and few-shot adaptation in optical network quality-of-transmission estimation without retraining models. By leveraging transferable feature representations, the method outperforms conventional machine learning baselines and contrastive learning approaches on cross-domain datasets. The work advances robust automation for optical networks by decoupling inference from source-specific decision boundaries through global features.
Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
arXiv:2608.00044v1 Announce Type: cross 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.
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.
5 Pages, 2 Figures. Accepted and presented at the 26th International Conference on Transparent Optical Networks (ICTON 2026), Prague, Czech Republic, 12-16 July 2026
Signal Processing (eess.SP); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
arXiv:2608.00044 [eess.SP]
(or arXiv:2608.00044v1 [eess.SP] for this version)
Full text under the publisher's open licence · 119 words
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