MétaCan
Menu
Back to cohort
Record W2089428086 · doi:10.5539/cis.v7n2p56

Phase Noise Jitter Synchronization for Coherent Optical OFDM via Pilot-Data-Aided and Wiener Filter

2014· article· en· W2089428086 on OpenAlexvenueno aff
Jean Temga, Mahamadou Hamidine, Carine H. Maiawe

Bibliographic record

VenueComputer and Information Science · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsnot available
FundersHuazhong University of Science and TechnologyNational Natural Science Foundation of China
KeywordsQuadrature amplitude modulationQAMMean squared errorMinimum mean square errorWiener filterOrthogonal frequency-division multiplexingComputer scienceModulation (music)Phase noiseEstimatorAlgorithmBit error rateElectronic engineeringMathematicsStatisticsPhysicsAcousticsEngineering

Abstract

fetched live from OpenAlex

modulation (M-QAM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) signal employing a unique pilot’s system design, Feed forward maximum likelihood phase estimator as well as Wiener filter-type Minimum Mean square error (MMSE) interpolator. The wiener filter relies upon Kolmogorov type to interpolate the estimated phase noise with M taps. A 20 Gb/s CO-OFDM via 4-QAM, 16-QAM, 64-QAM then 256-QAM modulation is applied as simulation model in Optisystem. System efficiency is evaluated throughout phase root mean square error (RMSE) calculated in degree. A comparative investigation of four different modulation techniques found that 4-QAM performs with good RMSE versus the rest of square M-QAM. A free-noise receiver, a pilot aided feed forward maximum likelihood (PA-FF-ML) receiver and a PA-FF-ML with MMSE (PA-FF-ML-MMSE) are compared. PA-FF-ML-MMSE exhibited superior performance rather than receiver using just PA-FF-ML.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.275

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 communication0.0000.003
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueComputer and Information ScienceSame topicOptical Network TechnologiesFrench-language works237,207