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Record W3008689558 · doi:10.1109/lpt.2020.2976622

On the Phase Noise Enhancement of a Continuous Wave in Saturated SOA Used for RIN Reduction

2020· article· en· W3008689558 on OpenAlexaff
Mustafa Al-Qadi, Maurice O’Sullivan, Chongjin Xie, Rongqing Hui

Bibliographic record

VenueIEEE Photonics Technology Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsCiena (Canada)
Fundersnot available
KeywordsLaser linewidthPhase noiseOpticsRelative intensity noiseNoise spectral densityOptical amplifierPhysicsNoise (video)Quantum noiseMaterials scienceOptoelectronicsSemiconductor laser theoryAmplifierLaserNoise figureComputer scienceQuantum

Abstract

fetched live from OpenAlex

We report on the spectral characteristics of the phase noise enhancement of continuous-wave (CW) optical signals passing through a semiconductor optical amplifier (SOA) operating in the saturation regime and used for relative intensity noise (RIN) reduction. We show that the known effect of phase noise enhancement, attributed to the linewidth enhancement factor of the device, happens only at a limited band of the phase noise spectrum, and the actual measurable linewidth of the output CW signal may not be affected. While this phase noise enhancement is not shown as an increase of spectral linewidth, it can still affect system performance when coherent detection is used, especially in applications with relatively low symbol rates. Numerical simulations and experimental results are used to support the observation. A single spectral line from a quantum-dot mode-locked laser is used as the light source, which is known to have relatively high RIN (> -120 dB/Hz in the low frequency region). Experimental transmission of 16-QAM modulation with coherent detection has been performed at 5 GBd to assess the implication on system performance.

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 score0.096
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.234
Teacher spread0.216 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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