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Record W2077806493 · doi:10.1109/lpt.2011.2164785

Noniterative Interpolation-Based Partial Phase Noise ICI Mitigation for CO-OFDM Transport Systems

2011· article· en· W2077806493 on OpenAlexaff
Mohammad E. Mousa-Pasandi, David V. Plant

Bibliographic record

VenueIEEE Photonics Technology Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingBit error rateQuadrature amplitude modulationComputer sciencePhase noiseInterpolation (computer graphics)Electronic engineeringQAMInterference (communication)Compensation (psychology)Signal-to-noise ratio (imaging)AlgorithmTelecommunicationsEngineeringDecoding methods

Abstract

fetched live from OpenAlex

We introduce and investigate the feasibility of a noniterative phase noise induced intercarrier interference (ICI) compensation scheme based on linear interpolation for coherent optical orthogonal frequency-division-multiplexing (CO-OFDM) transport systems. The study of bit-error-rate (BER) performance for a 40-Gb/s 16-quadrature amplitude modulation (QAM) CO-OFDM signal demonstrates a significant improvement versus the conventional equalizer (CE) in the optical signal-to-noise ratio (OSNR) requirement. Moreover, the capability of this equalizer in conjunction with the back-propagation (BP) nonlinearity compensation scheme is investigated and a brief analysis of the computational complexity, in terms of the number of required complex multiplications, is provided. This practical approach does not suffer from error propagation while enjoying low computational complexity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
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 head0.018
GPT teacher head0.249
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 source (direct Gemma or distilled Codex), 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

Citations28
Published2011
Admission routes1
Has abstractyes

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