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Record W2127735659 · doi:10.1109/25.892599

On the use of Cann's model for nonlinear behavioral-level simulation

2000· article· en· W2127735659 on OpenAlexaff
Sergey Loyka

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsIntermodulationNonlinear systemBehavioral modelingComputer scienceElectronic engineeringTaylor seriesChannel (broadcasting)Electronic circuitPower (physics)EngineeringTelecommunicationsElectrical engineeringMathematicsPhysicsAmplifier

Abstract

fetched live from OpenAlex

Behavioral level techniques are presently very popular tools for the nonlinear simulation of mobile communication circuits and systems. The use of Cann's (1980) model for modeling nonlinear circuits and systems is discussed in this paper. It is shown that this model has a nonphysical behavior for the small-signal regime in most cases (it cannot be expanded in a Taylor series) and gives incorrect predictions for nonlinear products. When nonlinearities are to be taken into account in the simulation (intermodulation, adjacent channel power, etc.), the use of Cann's model should be avoided or at least it should be used with extreme care.

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: none
Teacher disagreement score0.596
Threshold uncertainty score0.437

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 head0.082
GPT teacher head0.284
Teacher spread0.202 · 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

Citations14
Published2000
Admission routes1
Has abstractyes

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