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Record W2039659962 · doi:10.1049/iet-com.2012.0709

Adaptive relaying scheme for cognitive radio networks

2013· article· en· W2039659962 on OpenAlexafffund
Wael Jaafar, Wessam Ajib, David Haccoun

Bibliographic record

VenueIET Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCognitive radioComputer scienceScheme (mathematics)Computer networkTelecommunicationsWirelessMathematics

Abstract

fetched live from OpenAlex

Cognitive radio (CR) systems allow unlicensed secondary users to transmit on the licensed frequency bands without degrading the licensed primary transmissions. Combining CR with other emerging transmission techniques, such as user cooperation may have many benefits on both the primary and secondary transmissions. In this study, the authors propose and investigate an adaptive relay‐based cooperation scheme for CR networks that improves the secondary outage performance, while respecting a primary outage probability threshold. The proposed adaptive scheme considers one multi‐antenna relay node that, by selecting the antenna(s) to use, can assist either the primary, the secondary or both transmissions simultaneously. Expressions of the conditional primary outage probability for Rayleigh fading channels are derived and used to investigate the associated power allocation problem. Simulation results show that both primary and secondary outage probabilities of the proposed scheme are significantly improved and outperform non‐cooperative and cooperative schemes given in the literature.

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: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.085
GPT teacher head0.312
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

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
GenreMethods

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

Citations7
Published2013
Admission routes2
Has abstractyes

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