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Record W2888910292 · doi:10.1109/lmwc.2018.2863720

Doherty Transmitter Based on Monopole Array Antenna Active Load Modulation

2018· article· en· W2888910292 on OpenAlexafffund
Yulong Zhao, Fadhel M. Ghannouchi, Mohamed Helaoui, Xiang Li, Xuekun Du, Weiwei Zhang, Thomas Apperley

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta Innovates - Technology Futures
KeywordsTransmitterAmplifierElectrical engineeringBandwidth (computing)Electronic engineeringEngineeringAntenna (radio)Radio transmitter designDipole antennaModulation (music)AcousticsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This letter presents a new antenna topology to implement the load modulation in the Doherty transmitter, which resulted in enhancing the bandwidth compared to previous works. The active load modulation is realized by using a monopole array antenna, which serves as both Doherty combiner and wave radiator. The load impedances of amplifiers can be dynamically tuned through the mutual coupling of the array elements. Detailed design equations and procedures are developed. For verification, a Doherty transmitter operating at 1.95-2.10 GHz has been designed and fabricated using GaN HEMT transistors. The prototype transmitter exhibited a power-added efficiency of 53% at peak power level and maintained an efficiency of 43% at 6.8-dB backoff, over the frequency band of 1.95-2.10 GHz.

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 score0.003

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.210
Teacher spread0.197 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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