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Record W2051383776 · doi:10.1364/oe.21.015237

Theoretical and experimental investigation of direct detection optical OFDM transmission using beat interference cancellation receiver

2013· article· en· W2051383776 on OpenAlexafffund
S. Alireza Nezamalhosseini, Lawrence R. Chen, Qunbi Zhuge, Mahdi Malekiha, Farokh Marvasti, David V. Plant

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrthogonal frequency-division multiplexingBit error rateOpticsSingle antenna interference cancellationQuadrature amplitude modulationBeat (acoustics)Compatible sideband transmissionOptical Carrier transmission ratesInterference (communication)MultiplexingElectronic engineeringSidebandPhysicsTelecommunicationsComputer scienceOptical fiberChannel (broadcasting)Radio over fiberRadio frequencyEngineeringDetector

Abstract

fetched live from OpenAlex

We theoretically and experimentally evaluate a beat interference cancellation receiver (BICR) for direct detection optical orthogonal frequency-division multiplexing (DD-OFDM) systems that improves the spectral efficiency (SE) by reducing the guard band between the optical carrier and the optical OFDM signal while mitigating the impact of signal-signal mixing interference (SSMI). Experimental results show that the bit-error-rate (BER) is improved by about three orders of magnitude compared to the conventional receiver after 320 km single-mode fiber (SMF) transmission for 10 Gb/s data with a 4-QAM modulation using reduced guard band single-sideband OFDM (RSSB-OFDM) signal with 1.67 bits/s/Hz SE.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 designBench or experimental
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

Citations45
Published2013
Admission routes2
Has abstractyes

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