Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling
Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling
We propose a technique for modeling erbium-doped fiber amplifiers (EDFAs) in optical fiber networks, where the amplifier unit is located at a distant node outside the laboratory. We collect data on an optical point-to-point link with the amplifier as the only amplification stage. Different amplifier operating points are modeled using probe signals and by adjusting the settings of the amplifier through a control network. The data are used to train a machine learning algorithm integrated within a physical EDFA model. The obtained mathematical model for the amplifier is used to model all amplifiers of a network and links with multiple amplification stages. To confirm the modeling accuracy, we thereafter predict and optimize launch power profiles of two selected links in the network of 439.4 km and 592.4 km lengths. Maximum/average channel optical signal-to-noise ratio prediction errors of 1.41/0.68 dB and 1.62/0.83 dB are achieved for the two multi-span systems, respectively, using the EDFA model trained on the single span system with margin-optimized launch power profiles. Up to 2.2 dB of margin improvements are obtained with respect to unoptimized transmission.
C192-C202
Jones, Rasmus T.
fdf5674e-9263-4612-b268-5d14d26753e7
Bottrill, Kyle R.H.
8c2e6c2d-9f14-424e-b779-43c23e2f49ac
Taengnoi, Natsupa
afc5fb3e-224b-43b3-a161-931ed77faec1
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Yankov, Metodi P.
3c2ea612-ce43-4771-a985-dab37b2f31cb
1 August 2023
Jones, Rasmus T.
fdf5674e-9263-4612-b268-5d14d26753e7
Bottrill, Kyle R.H.
8c2e6c2d-9f14-424e-b779-43c23e2f49ac
Taengnoi, Natsupa
afc5fb3e-224b-43b3-a161-931ed77faec1
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Yankov, Metodi P.
3c2ea612-ce43-4771-a985-dab37b2f31cb
Jones, Rasmus T., Bottrill, Kyle R.H., Taengnoi, Natsupa, Petropoulos, Periklis and Yankov, Metodi P.
(2023)
Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling.
Journal of Optical Communications and Networking, 15 (8), .
(doi:10.1364/JOCN.480557).
Abstract
We propose a technique for modeling erbium-doped fiber amplifiers (EDFAs) in optical fiber networks, where the amplifier unit is located at a distant node outside the laboratory. We collect data on an optical point-to-point link with the amplifier as the only amplification stage. Different amplifier operating points are modeled using probe signals and by adjusting the settings of the amplifier through a control network. The data are used to train a machine learning algorithm integrated within a physical EDFA model. The obtained mathematical model for the amplifier is used to model all amplifiers of a network and links with multiple amplification stages. To confirm the modeling accuracy, we thereafter predict and optimize launch power profiles of two selected links in the network of 439.4 km and 592.4 km lengths. Maximum/average channel optical signal-to-noise ratio prediction errors of 1.41/0.68 dB and 1.62/0.83 dB are achieved for the two multi-span systems, respectively, using the EDFA model trained on the single span system with margin-optimized launch power profiles. Up to 2.2 dB of margin improvements are obtained with respect to unoptimized transmission.
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JOCN_remote_edfa-1
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jocn-15-8-c192
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Accepted/In Press date: 20 March 2023
e-pub ahead of print date: 22 June 2023
Published date: 1 August 2023
Additional Information:
Funding Information:
Engineering and Physical Sciences Research Council (EPSRC) (EP/S002871/1, EP/S028854/1); Innovationsfonden project RANON (1047-00013); Danish National Research Foundation (DNRF) Centre of Excellence Silicon Photonics for Optical Communications (SPOC) (DNRF123)
Identifiers
Local EPrints ID: 481032
URI: http://eprints.soton.ac.uk/id/eprint/481032
ISSN: 1943-0620
PURE UUID: 772ba5c0-ee79-44a9-bfec-9c152841c137
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Date deposited: 15 Aug 2023 16:36
Last modified: 18 Mar 2024 03:34
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Contributors
Author:
Rasmus T. Jones
Author:
Natsupa Taengnoi
Author:
Metodi P. Yankov
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