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Title: Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling
Optical networks satisfy high bandwidth and low latency requirements for telecommunication networks and data center interconnection. To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system performance such as quality of transmission. However, a comprehensive measurement dataset is required for ML to accurately predict an EDFA’s wavelength-dependent gain. We present an open dataset consisting of 202,752 gain spectrum measurements collected from 16 commercial-grade reconfigurable optical add–drop multiplexer (ROADM) booster and pre-amplifier EDFAs under varying gain settings and diverse channel-loading configurations over 2,785 hours in total, with a total dataset size of 3.1 GB. With this EDFA dataset, we implemented component-level deep-neural-network-based EDFA models and use transfer learning (TL) to transfer the EDFA model among 16 ROADM EDFAs, which achieve less than 0.18/0.24 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target training set. We also showed that TL reduces the EDFA data collection requirements on a new gain setting or a different type of EDFA on the same ROADM.  more » « less
Award ID(s):
2029295 2211944 1827923
PAR ID:
10438668
Author(s) / Creator(s):
; ;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Journal of Optical Communications and Networking
Volume:
15
Issue:
9
ISSN:
1943-0620; JOCNBB
Format(s):
Medium: X Size: Article No. 588
Size(s):
Article No. 588
Sponsoring Org:
National Science Foundation
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