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Record W2004646644 · doi:10.1364/oe.21.028668

Ideal optical backpropagation of scalar NLSE using dispersion-decreasing fibers for WDM transmission

2013· article· en· W2004646644 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.

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWavelength-division multiplexingOpticsBackpropagationOptical fiberDispersion (optics)Transmission (telecommunications)Nonlinear systemOptical amplifierElectronic engineeringPhysicsWavelengthComputer scienceTelecommunicationsEngineeringArtificial neural network

Abstract

fetched live from OpenAlex

An ideal optical backpropagation (OBP) scheme to compensate for dispersion and nonlinear effects of the transmission fibers is proposed. The scheme consists of an optical phase conjugator (OPC), N spans of dispersion-decreasing fibers (DDFs) and amplifiers, placed at the end of the fiber optic link. In order to compensate for the nonlinear effects of the transmission fibers exactly, the nonlinear coefficient of the backpropagation fiber has to increase exponentially with distance or equivalently the power in the backpropagation fiber should increase exponentially with distance if the nonlinear coefficient is constant. In this paper, it is shown that a combination of DDFs and amplifiers can compensate for the nonlinear effects exactly. An analytical expression for the dispersion profile of the DDF is derived. Numerical simulation of a long haul wavelength division multiplexing (WDM) fiber optic system with the proposed OBP scheme shows that the system reach can be enhanced by 54% as compared to digital backpropagation (DBP).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.692

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 science0.0000.000
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 head0.014
GPT teacher head0.228
Teacher spread0.215 · 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