A new retrieval-based framework has been developed for estimating quality-of-transmission in optical networks. This framework utilizes transferable feature representations to achieve cross-domain generalization without needing to retrain models, supporting both zero-shot and few-shot adaptation. Experiments on relevant datasets indicate that this approach outperforms traditional machine learning baselines and recent contrastive learning methods, suggesting its utility for automating optical networks. AI
IMPACT This framework could improve the efficiency and automation of optical networks by enabling better quality-of-transmission estimation.
RANK_REASON The cluster contains an academic paper detailing a new technical framework. [lever_c_demoted from research: ic=1 ai=0.7]
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