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Record W2055109912 · doi:10.1109/glocom.2012.6503290

Three regions for space-time spectrum sensing and access in cognitive radio networks

2012· article· en· W2055109912 on OpenAlexaff
Zhiqing Wei, Zhiyong Feng, Qixun Zhang, Wei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCognitive radioWhite spacesFalse alarmComputer scienceInterference (communication)TransmitterSpectrum (functional analysis)Dimension (graph theory)Radio spectrumTelecommunicationsComputer networkArtificial intelligenceWirelessMathematicsPhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In order to improve the spectrum utilization in cognitive radio networks, the spectrum holes in space-time-frequency multiple dimensions should be exploited accurately and efficiently. Therefore, a novel three region scheme, which includes the black region, grey region and white region, has been designed and proposed with one primary transmitter at the center, taking into account key interference factors from secondary users (SUs) and the miss detection and false alarm probabilities in spectrum sensing. Between the black region where only primary users (PUs) have exclusive right to use the spectrum and the white region where SUs can utilize the same spectrum without causing severe interference to PUs, the grey region has been designed, which has temporal spectrum access opportunities in time dimension once neglected by existing works. Moreover, the condition of the existence of a transition zone between grey region and white region is analyzed with theoretical results, where power control should be applied to SUs. The closed-form bounds of three regions are obtained, which can be used in the space-time spectrum sensing and access in cognitive radio networks.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations13
Published2012
Admission routes1
Has abstractyes

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