MétaCan
Menu
Back to cohort
Record W2794399848 · doi:10.1109/lpt.2018.2813164

PDL Impact on Linearly Coded Digital Phase Conjugation Techniques in CO-OFDM Systems

2018· article· en· W2794399848 on OpenAlexafffund
Sunish Kumar Orappanpara Soman, Abdelkerim Amari, Octavia A. Dobre, R. Venkatesan

Bibliographic record

VenueIEEE Photonics Technology Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMemorial University of Newfoundland
FundersAtlantic Canada Opportunities Agency
KeywordsOrthogonal frequency-division multiplexingOrthogonal polarization spectral imagingPolarization (electrochemistry)MultiplexingElectronic engineeringPhase (matter)Computer scienceOutage probabilityOpticsTopology (electrical circuits)MathematicsPhysicsTelecommunicationsEngineeringFadingDecoding methodsCombinatorics

Abstract

fetched live from OpenAlex

We investigate the impact of polarization-dependent loss (PDL) on the linearly coded digital phase conjugation (DPC) techniques in coherent optical orthogonal frequency-division multiplexing superchannel systems. We consider two DPC approaches: one uses orthogonal polarizations to transmit the linearly coded signal and its phase conjugate, while the other uses two orthogonal time slots of the same polarization. We compare the performances of these DPC approaches by considering both aligned- and statistical-PDL models. The investigation with an aligned-PDL model indicates that the latter approach is more tolerant to PDL-induced distortions when compared with the former. Furthermore, the study using the statistical-PDL model shows that the outage probability of the latter approach tends to zero at a root mean square PDL value of 3.6 dB. On the other hand, the former shows an outage probability of 0.63 for the same PDL value.

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 categoriesMeta-epidemiology (narrow)
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 score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.276
Teacher spread0.267 · 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.

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

Citations2
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Photonics Technology LettersSame topicOptical Network TechnologiesFrench-language works237,207