MétaCan
Menu
Back to cohort
Record W2553813946 · doi:10.1109/jsen.2016.2627884

Joint Optimal Transmission Power and Sensing Time for Energy Efficient Spectrum Sensing in Cognitive Radio System

2016· article· en· W2553813946 on OpenAlexaff
Faroq Awin, Esam Abdel‐Raheem, Majid Ahmadi

Bibliographic record

VenueIEEE Sensors Journal · 2016
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCognitive radioTransmission (telecommunications)Joint (building)Computer scienceEnergy (signal processing)Computational complexity theoryPower (physics)Efficient energy useIterative methodElectronic engineeringMathematical optimizationAlgorithmReal-time computingTelecommunicationsWirelessEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Joint optimal transmission power and sensing time of energy efficient spectrum sensing for a cognitive radio (CR) system is considered. The design problem is formulated as a function of two variables subjected to primary user protection constraints. An iterative algorithm with low computational complexity is proposed to determine the joint optimal transmission power and sensing time that maximizes the energy efficiency for a single CR system. Simulation results of the proposed algorithm are provided, evaluated, and compared with those of existing algorithms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicCognitive Radio Networks and Spectrum SensingFrench-language works237,207