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Record W2965638029 · doi:10.1109/tvt.2019.2931949

Spectrum Sensing Based on Maximum Generalized Correntropy Under Symmetric Alpha Stable Noise

2019· article· en· W2965638029 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 Transactions on Vehicular Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of British Columbia
FundersHigher Education Discipline Innovation ProjectChina Scholarship CouncilChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsRobustness (evolution)Gaussian noiseSignal-to-noise ratio (imaging)Spectrum (functional analysis)AlgorithmGaussianComputer scienceNoise (video)Spread spectrumConjugate gradient methodMathematicsElectronic engineeringEngineeringArtificial intelligenceTelecommunicationsPhysicsCode division multiple access

Abstract

fetched live from OpenAlex

In this correspondence paper, we address the spectrum sensing problem under the non-Gaussian noise scenario characterized by the symmetric alpha stable (SαS) model. In this paper, a novel spectrum sensing method is proposed using the maximum generalized correntropy, aimed at improving the spectrum sensing performance in low generalized signal-to-noise ratio conditions. To further enhance the robustness of the proposed method, multiple receive antennas are applied to carry out cooperative spectrum sensing. Besides, a modified conjugate gradient algorithm is used to optimize the sparse vector for cooperative spectrum sensing. Finally, simulation results are presented to verify the effectiveness of the proposed method.

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 score0.792
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.214
Teacher spread0.205 · 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