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Record W1972491259 · doi:10.1002/ett.1470

Linear combination‐based energy detection algorithm in low signal‐to‐noise ratio for cognitive radios

2011· article· en· W1972491259 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

VenueEuropean Transactions on Telecommunications · 2011
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsCognitive radioFalse alarmEnergy (signal processing)AlgorithmComputer scienceDetection theoryStatistical powerSIGNAL (programming language)Noise (video)Signal-to-noise ratio (imaging)TelecommunicationsArtificial intelligenceMathematicsWirelessStatisticsDetector

Abstract

fetched live from OpenAlex

Abstract This paper considers the spectrum sensing technique for cognitive radio‐based on energy detection. It proposes an improved spectrum sensing algorithm by linearly combining the criteria of the probability of detection and the probability of false alarm. Then, the optimal decision threshold is derived for energy detection in the proposed linear combination (LC) algorithm. The performance of primal user detection is significantly improved by using the proposed LC algorithm, especially under low signal‐to‐noise ratios. Simulation results verify the effectiveness of the proposed algorithm with different kinds of primary user signals. Copyright © 2011 John Wiley & Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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