Pith. sign in

REVIEW

Estimating Quality of Transmission in a Live Production Network using Machine Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2112.04031 v1 pith:RP2HDSU5 submitted 2021-12-07 cs.NI

Jasper Müller , Tobias Fehenberger , Sai Kireet Patri , Kaida Kaeval , Helmut Griesser , Marko Tikas , Jörg-Peter Elbers This is my paper
classification cs.NI
keywords livenetworkconfigurationdatademonstrateerrorestimatingestimation
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We demonstrate QoT estimation in a live network utilizing neural networks trained on synthetic data spanning a large parameter space. The ML-model predicts the measured lightpath performance with <0.5dB SNR error over a wide configuration range.

Discussion (0). Sign in to comment.

Pith tools