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Record W4400409408 · doi:10.1109/lmwt.2024.3420398

A Residual Selectable Modeling Method Based on Deep Neural Network for Power Amplifiers With Multiple States

2024· article· en· W4400409408 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Microwave and Wireless Technology Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsResidualArtificial neural networkAmplifierPower (physics)Computer scienceDeep neural networksArtificial intelligenceElectronic engineeringEngineeringTelecommunicationsAlgorithmPhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

A traditional power amplifier (PA) behavioral model typically represents one specific operating state of the PA. As the number of states of PA increases, the depth of the behavioral model based on the deep neural network (DNN) deepens. However, the deepening of the DNN may result in decreased model accuracy. To solve this issue, this letter proposes a residual selectable modeling method to obtain the residual DNN (RDNN), which can be used to build the multistate PA behavioral model. Experimental results show that the multistate PA model constructed by the proposed method can improve the accuracy of the DNN-based PA model. Also, the model accuracy does not decrease with the deepening of DNNs.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.593
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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