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Record W2114594874 · doi:10.1109/jlt.2005.863265

Higher bit rates for dispersion-managed soliton communication systems via constrained coding

2006· article· en· W2114594874 on OpenAlexaff
Vladimir Pechenkin, Frank R. Kschischang

Bibliographic record

VenueJournal of Lightwave Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJitterComputer scienceCoding (social sciences)Electronic engineeringCommunications systemOptical communicationChannel (broadcasting)Bit error rateDispersion (optics)AlgorithmTheoretical computer scienceTopology (electrical circuits)TelecommunicationsPhysicsOpticsMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Constrained coding as a method to increase the data rate in dispersion-managed soliton (DMS) communication systems is proposed. This approach is well known and widely used in the context of magnetic and optical recording systems. This paper shows that it is also applicable to DMS systems due to certain similarities between the underlying physical channels. Since timing jitter is an important error-generating mechanism for solitons, a coding scheme specifically designed to combat pulse shifts is also presented, and its properties in the framework of a particular information-theoretic channel model are analyzed. A connection between the model used and the real physical channel is then established. Next, the coded system is compared with the original one from the channel capacity point of view with the help of numerical examples. Finally, the fact that the application of constrained coding may alleviate soliton pulse-to-pulse interaction is exploited. This, in turn, opens the door to the usage of higher-than-usual map strengths and ultimately leads to a significant increase of up to 50% in the bit rate

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 designTheoretical or conceptual
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

Citations5
Published2006
Admission routes1
Has abstractyes

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