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Record W3133987038 · doi:10.1109/iws49314.2020.9359947

Bandwidth Enhanced Doherty Power Amplifier Based on Coupled Phase Compensation Network With Specific Optimal Impedance

2020· article· en· W3133987038 on OpenAlexaff
Xin Yu Zhou, Wing Shing Chan, Wenjie Feng, Xiao Hu Fang, Tushar Sharmar, Zheng Liu

Bibliographic record

Venue2020 IEEE MTT-S International Wireless Symposium (IWS) · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmplifierBandwidth (computing)WidebandImpedance matchingHigh-electron-mobility transistorElectrical impedanceElectronic engineeringComputer scienceTransistorElectrical engineeringBandwidth extensionEngineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This paper presents a new technique to dramatically enhance the bandwidth of a post-matching Doherty power amplifier (PM-DPA). This technique relies on using a coupled-line phase compensation network (PCN). Through a theoretical analysis, we first point out this coupled-line PCN can offer wideband impedance matching due to its capacity in reducing the external Q-factor of the peaking branch. Then, our analysis guides the derivation of closed-form formulas that help to determine the physical parameters of the coupled PCN. Finally, when augmented with suitable selected peaking amplifier impedances, the proposed technique allows improved peak efficiency and output power over extended bandwidth. For experimental verification, A DPA based on commercially available GaN high-electron-mobility transistor (HEMT) devices (Cree CGH 40010F) has been designed and fabricated. Measured results of the proposed DPA shows 6 dB back-off operation between 1.3 - 2.3 GHz (55% fractional bandwidth) with efficiency in excess of 41%.

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: Simulation or modeling · Consensus signal: none
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)0.0010.000
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)0.0010.000

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 head0.011
GPT teacher head0.233
Teacher spread0.221 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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