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Record W4392472153 · doi:10.1364/ol.517409

Extending the reach of multi-core fiber via Voronoi constellations with concatenated multilevel coding

2024· article· en· W4392472153 on OpenAlexaff
Can Zhao, Bin Chen, Yi Lei, Shen Li, Jiaqi Cai, Daohui Hu, Wenkai Fang, Lin Sun

Bibliographic record

VenueOptics Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversité Laval
FundersNational Natural Science Foundation of China
KeywordsCoding (social sciences)Voronoi diagramConstellationComputer scienceCore (optical fiber)FiberMathematicsTelecommunicationsMaterials sciencePhysicsStatisticsComposite materialGeometry

Abstract

fetched live from OpenAlex

This Letter proposes a novel, to the best of our knowledge, coded modulation scheme for randomly coupled multi-core fiber (RC-MCF) via multidimensional (MD) constellation with concatenated two-level multilevel coding (MLC). In the proposed system, the 16-dimensional (16D) Voronoi constellation (VC), naturally fitting with the 16 degrees of freedom of a four-core fiber (two quadratures, two polarizations, and four cores), is generated by a latticed-based shaping method to provide higher shaping gains. Moreover, combining it with the concatenated two-level MLC can further achieve better performance-complexity trade-off. It is demonstrated by simulation results of long-haul multi-channel RC-MCF transmission that the proposed coded modulation scheme for four-core fiber transmission offers 77% reduction in the number of decoding operations and up to 21% (585 km) reach increase over the conventional bit-interleaved coded modulation scheme for quadrature amplitude modulation.

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 score0.277
Threshold uncertainty score0.458

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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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