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Record W2516065681 · doi:10.1109/mwsym.2016.7540189

A concurrent 2.15/3.4 GHz dual-band Doherty power amplifier with extended fractional bandwidth

2016· article· en· W2516065681 on OpenAlexaff
Mingming Liu, Hamed Golestaneh, Slim Boumaiza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMulti-band deviceAmplifierParasitic extractionBandwidth (computing)Electrical engineeringElectronic engineeringElectrical impedanceTransistorComputer scienceEngineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

A novel output-combining network for a concurrent dual-band Doherty power amplifier (DPA) is proposed. The proposed output combiner employs a modified II-network, which enables the absorption of main and peaking transistors' output parasitics over an extended frequency range and eliminates the need for offset lines that compromise the achievable bandwidth. In addition to performing the impedance inversion, the proposed combiner incorporates the biasing feeds and presents small low-frequency impedances to both transistors in order to improve the DPA linearizability when driven with concurrent dual-band modulated signals. A 50-W dual-band DPA demonstrator was successfully designed to operate across two bands, 2.05-2.30GHz and 3.2-3.62GHz. It achieved greater than 49% and 47% drain efficiency at a 6-dB output back-off over the two bands, and was successfully linearized under single and dual-band modulated signal stimuli.

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)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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