Abstract:
We train language models to automate the diagnosis of OTN configuration errors, and the diagnostic accuracy is up to 97.56%. We additionally demonstrate the effectiveness...Show MoreMetadata
Abstract:
We train language models to automate the diagnosis of OTN configuration errors, and the diagnostic accuracy is up to 97.56%. We additionally demonstrate the effectiveness of the models on a real OTN system.
Date of Conference: 05-09 March 2023
Date Added to IEEE Xplore: 19 May 2023
Print on Demand(PoD) ISBN:979-8-3503-1229-4