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Record W2104417207 · doi:10.1504/ijaacs.2009.024281

Differential sensing in cognitive personal area networks with bursty secondary traffic and varying primary activity factor

2009· article· en· W2104417207 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

VenueInternational Journal of Autonomous and Adaptive Communications Systems · 2009
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceCognitive radioChannel (broadcasting)IdleProbabilistic logicSet (abstract data type)Transmission (telecommunications)Computer networkDifferential (mechanical device)Real-time computingTelecommunicationsWirelessArtificial intelligence

Abstract

fetched live from OpenAlex

Cognitive radio technology necessitates accurate and timely sensing of primary users' activity on the chosen set of channels. We assume that the number of sensing nodes is smaller than the number of channels; the results of sensing are cooperatively combined to form a coherent channel map. Since each secondary user has its own bursty data traffic, it is unavailable for sensing until that data is transmitted. Furthermore, we assume that idle channels ought to be sensed more frequently than the active ones, so that the end of transmission opportunities are sensed with greater accuracy. The article presents a probabilistic analysis of the sensing mechanism under these circumstances, and investigates the range of parameter values in which the sensing process is capable of maintaining an accurate view of the status of the working channel set.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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