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Record W2120600776 · doi:10.1109/mnrc.2008.4683364

Fully-integrated CMOS bidirectional distributed amplifier as tunable active duplexer for wireless transceiver applications

2008· article· en· W2120600776 on OpenAlexaff
Ziad El-Khatib, Leonard MacEachern, S. Mahmoud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsCarleton University
Fundersnot available
KeywordsDuplexerCMOSAmplifierChipIntegrated circuit designElectrical engineeringBandwidth (computing)Computer scienceTopology (electrical circuits)Electronic engineeringAntenna (radio)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

The design of a fully-integrated CMOS bidirectional distributed amplifier (DA) based active duplexer with tunable broadband high isolation capability is presented. The S21differential power gain peaks at 7 dB and then rolls off to a unity gain bandwidth of 11.5 GHz. The simulated tunable isolation performance is better than -26 dB. The simulated tunable isolation S31show a 10 dB improvement. Simulation results show that the co-design of DA based active duplexer with on-chip loop antenna has 6 dB S21power gain added improvement from DC up to 5.2 GHz compared to a matched stand alone on-chip loop antenna S21power performance. The CMOS bidirectional DA based tunable active duplexer was fabricated using the 0.13 mum CMOS technology and has a total silicon chip area of 1.887times0.795 mm2.

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: Empirical
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 communication0.0000.001
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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