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Record W2039667894 · doi:10.1109/wowmom.2012.6263755

Double-layer dynamics of cognitive radio networks

2012· article· en· W2039667894 on OpenAlexaff
Peyman Setoodeh, S. Haykin, Keyvan R. Moghadam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCognitive radioWirelessDependency (UML)Computer scienceCognitionWireless networkScale (ratio)TelecommunicationsArtificial intelligenceGeographyPsychology

Abstract

fetched live from OpenAlex

There are two worlds of wireless communications: the legacy wireless world and the cognitive wireless world. Spectrum holes are the medium through which the two worlds interact. Releasing subbands by primary users allows the cognitive radio users to perform their normal tasks and, therefore, to survive. In other words, the old world affects the new world through appearance and disappearance of the spectrum holes and there is a master-slave relationship between them. Hence, the two worlds of wireless communications are going on side by side. This makes a cognitive radio network a multiple-time-scale dynamic system: a large-scale time in which the activities of primary users change and a small-scale time in which the activities of secondary users change accordingly. Such systems are called double-layer dynamic systems. A model is built that can be used as a testing tool for policy forecast and incorporates time evolution as the life span (control horizon) of a given policy. Two types of time dependency are studied: time-dependent equilibria and time-dependent behaviour away from the predicted curve of equilibria. Theories of evolutionary variational inequalities and projected dynamic systems on Hilbert spaces are used to study these two types of time dependency, respectively.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.511

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 integrity0.0000.000
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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations3
Published2012
Admission routes1
Has abstractyes

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