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Record W4389478942 · doi:10.1364/oe.509643

Performance of quantum-dash mode-locked lasers (QD-MLLDs) for high-capacity coherent optical communications

2023· article· en· W4389478942 on OpenAlexfundno aff
Mostafa Khalil, Yuxuan Xie, Essam Berikaa, Jiaren Liu, Zhenguo Lü, Philip J. Poole, Guocheng Liu, John Weber, David V. Plant, Lawrence R. Chen

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsLaser linewidthOpticsQuadrature amplitude modulationPhase noiseLaserOptoelectronicsPhysicsOptical communicationSemiconductor laser theoryFrequency combMaterials scienceTelecommunicationsBit error rateComputer science

Abstract

fetched live from OpenAlex

We investigate the capabilities and limitations of quantum-dash mode-locked lasers (QD-MLLDs) as optical frequency comb sources in coherent optical communication systems. We demonstrate that QD-MLLDs are on par with conventional single-wavelength narrow linewidth laser sources and can support high symbol rates and modulation formats. We manage to transmit 64 quadrature amplitude modulation (QAM) signals up to 80 GBd over 80 km of standard single-mode fiber (SSMF), which highlights the distinctive phase noise performance of the QD-MLLD. Using a 38.5 GHz (6 dB bandwidth) silicon photonic (SiP) modulator, we achieve a maximum symbol rate of 104 GBd with 16QAM signaling and a maximum net rate of 416 Gb/s per carrier in a single polarization setup and after 80 km-SSMF transmission. We also compare QD-MLLD performance with commercial narrow-linewidth integrable tunable laser assemblies (ITLAs) and explore their potential for use as local oscillators (LOs) and signal carriers. The QD-MLLD has 45 comb lines usable for transmission at a frequency spacing of 25 GHz, and an RF linewidth of 35 kHz.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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 designTheoretical or conceptual
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

Citations7
Published2023
Admission routes1
Has abstractyes

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