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Record W2611938955 · doi:10.1109/lpt.2017.2700758

Data Rate Quadrupled Coherent Microwave Photonic Link

2017· article· en· W2611938955 on OpenAlexafffund
Xiang Chen, Jianping Yao

Bibliographic record

VenueIEEE Photonics Technology Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceElectronic engineeringLocal oscillatorMicrowaveDigital signal processingPhase noiseForward error correctionQuadrature amplitude modulationOpticsBit error ratePhysicsTelecommunicationsEngineeringComputer hardware

Abstract

fetched live from OpenAlex

A data rate quadrupled microwave photonic link (MPL) based on coherent detection and digital signal processing (DSP) is proposed and experimentally demonstrated. The data rate is quadrupled by employing both intensity and phase modulation and polarization multiplexing to transmit four microwave vector signals with an identical microwave center frequency over a single optical carrier. The recovery of the four microwave signals is implemented at a coherent receiver followed by DSP with a new algorithm, to separate the four microwave vector signals and to eliminate the phase noise from the transmitter and local oscillator laser sources and to cancel the unstable frequency offset between the two laser sources. Error-free transmission of four 2.5-Gpbs 16-QAM microwave vector signals with a total data rate of 10 Gbps if forward error correction (FEC) is employed. The total net data rate is 9.225 Gbps if the zero padding and the FEC (overhead 6.7%) are considered. The use of the proposed MPL for the implementation of 4 × 4 MIMO is also discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.288
Teacher spread0.249 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

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