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Record W2920798028 · doi:10.1109/istel.2018.8661035

Blind Discrete-Time Cyclostationary Spectrum Sensing with Multiple Primary Users in Presence of Spatially and Temporally Correlated Noise

2018· article· en· W2920798028 on OpenAlexaff
Shahrzad Safdari, Abbas Taherpour, Saeed Gazor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsQueen's University
Fundersnot available
KeywordsCyclostationary processDetectorNoise (video)Computer scienceLikelihood-ratio testAntenna (radio)Signal-to-noise ratio (imaging)AlgorithmSpectrum (functional analysis)StatisticsElectronic engineeringMathematicsArtificial intelligenceTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

We propose a multi-antenna cyclostationary based spectrum sensing technique, which is blind with respect to the cyclic frequencies (CF) and number of active primary users (PU). The general model of noise with no prior specific structure is assumed and hence the proposed method can be employed even in the presence of spatially or temporally correlated noise environments. The presented detector is derived by calculating the decision statistics based on generalized likelihood ratio test (GLRT) for different CFs corresponding to PUs. Simulation results illustrate the superiority of proposed detector compared to the other multi-antenna cyclostationary based detectors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.602

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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