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Record W2756984695 · doi:10.1109/iscas.2017.8050880

Highly linear reconfigurable mixer designed for environment-aware receiver

2017· article· en· W2756984695 on OpenAlexaff
Mohammad-Mahdi Mohsenpour, Carlos E. Saavedra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsQueen's University
Fundersnot available
KeywordsFrequency mixerLinearityHarmonic mixerElectronic mixerCMOSElectrical engineeringElectronic engineeringPower (physics)Power consumptionRadio frequencyComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

A 4-state, highly linear, reconfigurable mixer is presented in this paper. The proposed mixer is an essential part of the environment-aware receiver, introduced in this article. A set of switches are used in the LO and IF stages of the mixer to digitally tune the linearity of the mixer in exchange for lower power consumption. Three sets of cross-coupled pairs are utilized in the mixer to dynamically inject the proper current into the mixer and improve the linearity of each state of the mixer. The proposed mixer is designed using a standard 130-nm CMOS process. The mixer delivers 10, 6.2, 1.8, and -2.9 dBm of third-order intercept point (IIP3) while the average NF is 9.3 dB and varies only 1 dB in different states. Conversion gain of the mixer varies within 16.8 to 7.8 dB in 0.5 to 7 GHz. The mixer consumes 2.4 to 9.6 mW from a 1.2V supply.

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 categoriesInsufficient 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 score0.942
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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