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Record W2124047962 · doi:10.1364/oe.18.003919

Data-aided adaptive weighted channel equalizer for coherent optical OFDM

2010· article· en· W2124047962 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrthogonal frequency-division multiplexingComputer sciencePhase noisePolarization mode dispersionAdaptive equalizerElectronic engineeringChannel (broadcasting)Equalization (audio)Transmission (telecommunications)TelecommunicationsOptical fiberEngineering

Abstract

fetched live from OpenAlex

We report an adaptive weighted channel equalizer (AWCE) for orthogonal frequency division multiplexing (OFDM) and study its performance for long-haul coherent optical OFDM (CO-OFDM) transmission systems. This equalizer updates the equalization parameters on a symbol-by-symbol basis thus can track slight drifts of the optical channel. This is suitable to combat polarization mode dispersion (PMD) degradation while increasing the periodicity of pilot symbols which can be translated into a significant overhead reduction. Furthermore, AWCE can increase the precision of RF-pilot enabled phase noise estimation in the presence of noise, using data-aided phase noise estimation. Simulation results corroborate the capability of AWCE in both overhead reduction and improving the quality of the phase noise compensation (PNC).

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.049
GPT teacher head0.276
Teacher spread0.227 · 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