Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices

Francesco Da Ros; Metodi P. Yankov; Uiara Celine de Moura

REVIEW

Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices

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 2009.05326 v1 pith:N2DIWDBD submitted 2020-09-11 eess.SP cs.LGphysics.optics

Francesco Da Ros , Uiara Celine de Moura , Metodi P. Yankov This is my paper

classification eess.SPcs.LGphysics.optics

keywords modeledfagainphysicalsameamplifierbuiltdevice

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

abstract

We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements. The model shows low gain-prediction error for both the same device used for training (MSE $\leq$ 0.04 dB$^2$) and different physical units of the same make (generalization MSE $\leq$ 0.06 dB$^2$).

Discussion (0). Sign in to comment.

Pith tools