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Record W2029605802 · doi:10.1109/mvt.2009.934672

Cognitive radio networks

2009· article· en· W2029605802 on OpenAlexaff
Xuemin Hong, Z. Chen, Cheng‐Xiang Wang, Sergiy A. Vorobyov, John Thompson

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.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.008

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

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 designTheoretical or conceptual
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

Citations123
Published2009
Admission routes1
Has abstractyes

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