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A 28 GHz Dual-Mode Power Amplifier for Enhanced Load Resiliency or Back-Off Efficiency Enhancement in 22NM FDSOI Process

2025· article· en· W4413122305 on OpenAlexaff
Hang Yu, Mehran Hazer Sahlabadi, Slim Boumaiza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceDual modeAmplifierOptoelectronicsPower (physics)Dual (grammatical number)Electrical engineeringElectronic engineeringProcess (computing)Silicon on insulatorComputer scienceCMOSEngineeringPhysicsSilicon

Abstract

fetched live from OpenAlex

This paper presents a dual-mode power amplifier (PA) with configurable gate biases, optimized for large-scale antenna array system (LSAAS) front-ends. In the first mode, the VSWR resiliency mode, the PA is configured to maximize resiliency to high load variations caused by antenna cross-coupling in LSAAS, by maintaining consistent power, linearity, and efficiency. In the second mode, the back-off (BO) efficiency enhancement mode, the PA is configured to improve BO efficiency when driven by complex modulated signals with high peak-to-average power ratio (PAPR), during minimal load variation. A 28 GHz circuit demonstrator is implemented using the 22 nm fully-depleted silicon-on-insulator (FD-SOI) CMOS process. Under a 50 -ohm load, the VSWR resiliency mode achieves 16.5 dB gain, 13 dBm output power, and 18 % peak power-added efficiency (PAE), while the BO efficiency enhanced mode delivers 14 dB gain, 13 dBm output power, with 17.5 % and 11 % PAE at peak and 6 dB back-off, respectively. When tested against a varying load with VSWR 2.5:1 over a 360° range, the VSWR resiliency mode shows an average saturated power loss of 0.5 dB, while the BO efficiency enhanced mode shows a 1 dB average loss. Modulated signal measurements further confirm the superior performance of the VSWR resiliency mode under load variation compared to the BO efficiency enhanced mode.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.904
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.299
Teacher spread0.288 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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