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Record W1544044607 · doi:10.1109/wowmom.2015.7158194

Unified approach for performance analysis of Cognitive Radio Spectrum Sensing over correlated multipath fading channels

2015· article· en· W1544044607 on OpenAlexaff
Salam Al-Juboori, Xavier Fernando

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFadingCognitive radioMultipath propagationNakagami distributionMaximal-ratio combiningAlgorithmDiversity schemeDiversity combiningProbability density functionComputer scienceSpectrum (functional analysis)Electronic engineeringMathematicsStatisticsTopology (electrical circuits)TelecommunicationsPhysicsEngineeringWirelessCombinatoricsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this work, we analyse the performance of Cognitive Radio Spectrum Sensing (CRSS) systems with multiple receiving antennas considering the effect of correlation among fading branches. Exact closed-form expressions for the average detection probabilities (P̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> ) are derived employing Probability Density Functions (PDF) approach for n.i.i.d.-L number of diversity branches over Nakagami-m fading channels with Maximal Ratio Combining (MRC) diversity. Performance analysis reveals the detrimental effect of the correlation on detection performance thus decreasing detection probability. However, results also show that this effect could be compensated through employing diversity combining technique and by increasing the diversity branches.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.254
Teacher spread0.220 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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