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Record W1972295349 · doi:10.1364/oe.22.000071

400 Gbit/s single-carrier and 1 Tbit/s three-carrier superchannel signals using dual polarization 16-QAM with look-up table correction and optical pulse shaping

2013· article· en· W1972295349 on OpenAlexaff
Jian Hong Ke, Ying Gao, John C. Cartledge

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsQuadrature amplitude modulationOpticsTerabitBandwidth (computing)QAMOptical amplifierPhysicsBit error rateElectronic engineeringComputer scienceTelecommunicationsWavelength-division multiplexingLaserEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

A 448 Gbit/s single-carrier dual-polarization 16-ary quadrature-amplitude-modulation (DP 16-QAM) signal and a 1.206 Tbit/s three-carrier DP 16-QAM signal are demonstrated using look-up table (LUT) correction and optical pulse shaping. The LUT correction is used to mitigate the effects of transmitter-based pattern-dependent distortion due to the high symbol rates. A programmable optical filter is employed to narrow the modulated signal spectrum and thereby enhance the spectral efficiency and reduce the requirements on the receiver bandwidth and analog-to-digital converter sampling rate. By combining these techniques, the back-to-back required optical signal-to-noise ratios are 26.6 dB and 27.2 dB for BER = 10(-3), and transmission over 1200 and 1500 km of standard single-mode fiber with EDFA amplification was achieved for the 448 Gbit/s signal (12% forward error correction (FEC) overhead) and 1.206 Tbit/s signal (20% FEC overhead), respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.213
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations80
Published2013
Admission routes1
Has abstractyes

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