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Record W2161134252 · doi:10.1109/lpt.2006.889104

A New Approach to Achieve High Spectral Efficiency in Wavelength-Time OCDMA Network Transmission

2007· article· en· W2161134252 on OpenAlexaff
Aminata A. Garba, Jan Bajcsy

Bibliographic record

VenueIEEE Photonics Technology Letters · 2007
Typearticle
Languageen
FieldEngineering
Topicgraph theory and CDMA systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceCode division multiple accessTransmission (telecommunications)Bit error rateSpectral efficiencyDemodulationChannel (broadcasting)TransmitterModulation (music)Interference (communication)Code (set theory)Electronic engineeringError detection and correctionReed–Solomon error correctionData transmissionForward error correctionDecoding methodsComputer networkAlgorithmConcatenated error correction codeTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

We design a new wavelength-time optical code-division multiple-access (OCDMA) modulation scheme that does not use spreading sequences for information transmission, i.e., the spreading length is N=1. The proposed transmitter sends (error-control) coded data directly through the optical channel and exploits a probabilistic method to reduce the amount of multiuser interference. Simulation results show that by using turbo codes, Reed-Solomon codes, and soft-decision demodulation, the proposed OCDMA scheme can support hundreds of active users at target bit-error rate =10-9. Furthermore, the achieved spectral efficiency of 0.740 bits per channel use is almost twice as large as the best previous OCDMA results

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: none
GenreCandidate signal: Empirical · Consensus signal: none
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
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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