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Record W2164408356 · doi:10.1109/iscas.2011.5937556

A compact CMOS UWB LNA using tunable active inductors for WLAN interference rejection

2011· article· en· W2164408356 on OpenAlexaff
Md. Mahbub Reja, I.M. Filanovsky, Kambiz Moez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorCMOSResonatorBandwidth (computing)Band-stop filterCapacitorWidebandPassbandBand-pass filterNoise figureLow-noise amplifierElectrical engineeringCenter frequencyAmplifierElectronic engineeringOptoelectronicsMaterials scienceTelecommunicationsComputer scienceLow-pass filterEngineering

Abstract

fetched live from OpenAlex

A compact 2.0-11.0GHz CMOS ultra-wideband (UWB) low-noise amplifier (LNA) using tunable active inductors for suppressing in-band (over 4.8-6.0 GHz) WLAN interference signals is presented. In the proposed LNA, the active inductor in series with a small capacitor forms an active LC resonator which rejects or notches the undesired signals at the resonance frequency. Employing multiple resonators in the LNA increases the rejection depth. Moreover, the tunability of the active inductors allows for notching the signals over a wide frequency range. Designed and simulated in a 90 nm digital CMOS process, the proposed LNA with such active inductors used in notch filters occupies a core chip-area of only 0.0182 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The LNA exhibits an average power gain of 16.5 dB over 2.0-11.0 GHz bandwidth while the rejection of unwanted WLAN interference signals is -44.8 dB at 5.81 GHz. The notch-frequency can be tuned in excess of 4.5-6.6 GHz, and the rejection depth can be increased to -87.5 dB, the highest rejection among the reported notch-filter UWB LNAs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.243
Teacher spread0.179 · 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.

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

Citations9
Published2011
Admission routes1
Has abstractyes

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