MétaCan
Menu
Back to cohort
Record W2155309582 · doi:10.1109/twc.2009.081586

Spectrum sensing in cognitive radio using goodness of fit testing

2009· article· en· W2155309582 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 Wireless Communications · 2009
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCognitive radioGoodness of fitComputer scienceSIGNAL (programming language)Channel (broadcasting)DetectorDetection theoryEnergy (signal processing)Anderson–Darling testTransmission (telecommunications)Spectrum (functional analysis)Signal-to-noise ratio (imaging)TelecommunicationsStatisticsWirelessMathematicsStatistical hypothesis testingTest statisticMachine learningPhysics

Abstract

fetched live from OpenAlex

One of the most important challenges in cognitive radio is how to measure or sense the existence of a signal transmission in a specific channel, that is, how to conduct spectrum sensing. In this letter, we first formulate spectrum sensing as a goodness of fit testing problem, and then apply the Anderson-Darling test, one of goodness of fit tests, to derive a sensing method called Anderson-Darling sensing. It is shown by both analysis and numerical results that under the same sensing conditions and channel environments, Anderson-Darling sensing has much higher sensitivity to detect an existing signal than energy detector-based sensing, especially in a case where the received signal has a low signal-to-noise ratio (SNR) without prior knowledge of primary user signals.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.071
GPT teacher head0.302
Teacher spread0.231 · 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