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Record W2170786135 · doi:10.1364/jocn.2.000701

Analysis of Four-Wave Mixing Suppression in Fiber-Optic OFDM Transmission Systems With an Optical Phase Conjugation Module

2010· article· en· W2170786135 on OpenAlexafffund
Vladimir Pechenkin, I.J. Fair

Bibliographic record

VenueJournal of Optical Communications and Networking · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFour-wave mixingSubcarrierOrthogonal frequency-division multiplexingPhase conjugationOpticsMixing (physics)Transmission (telecommunications)Optical fiberMultiplexingPhase (matter)Multi-mode optical fiberElectronic engineeringWavelength-division multiplexingOptical powerComputer sciencePhysicsNonlinear opticsTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Coherent optical orthogonal frequency-division multiplexed (OFDM) systems must be carefully designed to minimize the detrimental impact of fiber nonlinearity manifested through four-wave mixing (FWM). Because of the small subcarrier spacing associated with OFDM, a significant fraction of FWM processes is well matched, resulting in a rapid buildup of FWM light with propagation distance. In this paper, we consider optical phase conjugation (OPC) as an approach to suppress such well-matched FWM processes. An analytical formula accurately predicting the degree of suppression is derived and discussed. It is shown that when combined with the methods previously proposed in the literature, the application of OPC can dramatically reduce the overall FWM power accumulated within the link for a wide range of crucial design parameters.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.275
Teacher spread0.242 · 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

Citations37
Published2010
Admission routes2
Has abstractyes

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