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Record W2064092307 · doi:10.1109/wcnc.2014.6952237

Transient analysis in cognitive radio collaborative spectrum sensing

2014· article· en· W2064092307 on OpenAlexaff
S. Ali Mousavifar, Cyril Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransient (computer programming)Cognitive radioComputer scienceExpression (computer science)Cascading Style SheetsTransient analysisEnergy (signal processing)Spectrum (functional analysis)Steady state (chemistry)Computational complexity theoryAlgorithmMathematicsTelecommunicationsPhysicsStatisticsChemistryWirelessWorld Wide Web

Abstract

fetched live from OpenAlex

A two-phase energy efficient collaborative spectrum sensing (CSS) strategy, namely EE-CSS, was proposed in [1], and expressions were derived for the steady-state (long term) average trust values and number of transmitted reports for honest as well as malicious secondary users. In this paper, we study the transient behavior of these quantities. A closed form expression is derived for the transient behavior of the average trust values. An event-based method for computing the average number of transmitted reports is described and its computational complexity is discussed. An approximate method as well as upper and lower bounds are proposed for the average number of transmitted reports. The results are verified using computer simulations.

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)0.001
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 score0.942
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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