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Record W2029605802 · doi:10.1109/mvt.2009.934672

Cognitive radio networks

2009· article· en· W2029605802 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Vehicular Technology Magazine · 2009
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
FundersDivision of Mathematical SciencesUniversity of EdinburghScottish Funding CouncilHeriot-Watt University
KeywordsCognitive radioExploitTransceiverRadio spectrumComputer scienceInterference (communication)Radio resource managementSpectrum managementComputer networkSoftware-defined radioTelecommunicationsResource (disambiguation)WirelessComputer securityWireless networkChannel (broadcasting)

Abstract

fetched live from OpenAlex

Radio spectrum is a scarce and precious natural resource that is significantly underutilized with current fixed spectrum-licensing policies [1]. This has inspired the development of hierarchical spectrum-sharing systems, where secondary systems are allowed to access the underutilized spectrum of incumbents without causing harmful interference to legacy/primary systems. In this article, we are interested in an important paradigm of secondary systems known as cognitive radio (CR) networks [2], [3], where the secondary terminals are envisioned to be capable of sensing and reasoning about the operating radio environments and thereby autonomously adjusting their transceiver parameters to exploit the underutilized radio resources in a dynamic fashion.

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.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
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
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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