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Record W2995214128 · doi:10.1109/jlt.2016.2523680

Postcompensation of Nonlinear Distortions of 64-QAM Signals in a Semiconductor-Based Wavelength Converter

2016· article· en· W2995214128 on OpenAlexafffund
B. Filion, An Nguyen, Leslie A. Rusch, Sophie LaRochelle

Bibliographic record

VenueJournal of Lightwave Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsQuadrature amplitude modulationQAMElectronic engineeringOptical amplifierWavelengthAmplifierOpticsBit error rateComputer sciencePhysicsBandwidth (computing)TelecommunicationsEngineeringLaser

Abstract

fetched live from OpenAlex

We experimentally investigate postcompensation of nonlinear distortions induced by a wavelength converter (WC) based on four-wave mixing in a semiconductor optical amplifier. The technique exploits a low-complexity digital filter-based backpropagation (DFBP) method. We perform postcompensation of nonlinear distortions following single stage wavelength conversion of 5 Gbd 64-quadrature amplitude modulation (QAM). We examine the DFBP performance in the presence of a degraded optical signal-to-noise ratio at the WC input, and explore the WC optimal operating conditions. Also, we experimentally demonstrate for the first time in the literature the dual stage wavelength conversion of QAM signals, in particular, 5 Gbd 64-QAM, and show that bit error rate below hard-decision forward error correction threshold is only possible with postcompensation of nonlinear distortions. These results are of importance for the development of wavelength-routed networks requiring successive wavelength conversion stages to enhance routing capabilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.220
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2016
Admission routes2
Has abstractyes

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