MétaCan
Menu
Back to cohort
Record W2070950990 · doi:10.1109/pimrc.2011.6139841

Outage performance in cooperative CDMA systems over nakagami-m fading channels

2011· article· en· W2070950990 on OpenAlexaff
Ali Mehemed, Walaa Hamouda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsNakagami distributionFadingCumulative distribution functionCode division multiple accessComputer scienceRandom variableExpression (computer science)Signal-to-noise ratio (imaging)Outage probabilityMaximal-ratio combiningTopology (electrical circuits)AlgorithmTelecommunicationsMathematicsElectronic engineeringProbability density functionStatisticsChannel (broadcasting)EngineeringCombinatorics

Abstract

fetched live from OpenAlex

The performance of decode-and-forward (DAF) cooperative code-division multiple-access (CDMA) systems is analyzed in Nakagami fading channels. A closed-form expression for the cumulative distribution function (CDF) is derived based on of the sum of two independent non-identical distributed gamma random variables (RV). This expression is then used to obtain the outage probability. In our analysis we consider both perfect and non-perfect inter-user channels. We also investigate the behavior of the system at high signal-to-noise ratio (SNR) where we obtain asymptotically the achievable diversity order for various system parameters. Simulation results are presented to assess the accuracy of our analytical results.

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)0.002
metaresearch head score (Gemma)0.011
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.075
GPT teacher head0.267
Teacher spread0.192 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicCooperative Communication and Network CodingFrench-language works237,207