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Record W2757036893 · doi:10.1109/iscas.2017.8050567

A sub-mW spectrum sensing architecture for portable IEEE 802.22 cognitive radio applications

2017· article· en· W2757036893 on OpenAlexaff
Kevin Banović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCognitive radioCapacitorTransceiverComputer scienceElectronic engineeringChipRadio frequencyCapacitive sensingElectrical engineeringComputer hardwareEngineeringWirelessTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

A low power integrating mixer successive approximation register (SAR) prototype chip for spectrum sensing is fabricated for portable transceivers targeting IEEE 802.22 cognitive radio applications. The integrating mixer SAR combines mixing, current-domain windowing, and integration to implement the short-time Fourier transform. Integration with programmable time constant is incorporated within the mixer by utilizing binary-weighted capacitive loads, which double as the sampling capacitors of a SAR ADC. The design operates over a frequency range of 0.05–1.25GHz, consumes 0.88mW from 1.1/1.2V supplies and obtains an average dynamic range (DR) of 25.7–27.9dB.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.943
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 studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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 designOther design
Domainnot available
GenreMethods

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

Citations6
Published2017
Admission routes1
Has abstractyes

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