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Record W1977759615 · doi:10.1364/ao.45.006812

Analysis and comparison of impairments in differential phase-shift keying and on-off keying transmission systems based on the error probability

2006· article· en· W1977759615 on OpenAlexaff
Xianming Zhu, Xun Li

Bibliographic record

VenueApplied Optics · 2006
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsKeyingPhase-shift keyingBit error rateDifferential phaseNoise (video)Transmission (telecommunications)Phase noiseOpticsAmplitude and phase-shift keyingPhysicsDispersion (optics)Nonlinear systemTelecommunicationsElectronic engineeringMathematicsComputer scienceDecoding methodsEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

The effect of nonlinear phase noise in dispersion-managed optical transmission systems is studied. The variance of the nonlinear phase noise in systems based on differential phase-shift keying (DPSK) in the presence of dispersion is examined analytically, and a semianalytical expression to calculate the error probability including intrachannel four-wave mixing, linear phase noise, and nonlinear phase noise for systems based on DPSK is derived. In addition, for the on-off keying (OOK) format, the formula for the error probability including amplified spontaneous emission noise and intrachannel nonlinear effects has been given. On the basis of the semianalytical expressions, we have compared the error probability of systems based on DPSK and OOK, and the results show that, to reach a given bit error rate of 10(-9) for a specific long-haul system, the difference between the signal-to-noise ratio required by the DPSK format and that required by the OOK format is around 6 dB for the launch power of 0 dBm, and the difference becomes larger as the launch power increases.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.527

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 head0.016
GPT teacher head0.247
Teacher spread0.230 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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