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Record W2033140050 · doi:10.1109/glocomw.2012.6477614

Dual-hop AF relaying systems in mixed nakagami-m and Rician links

2012· article· en· W2033140050 on OpenAlexaff
Samy S. Soliman, Norman C. Beaulieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRician fadingNakagami distributionFadingCumulative distribution functionProbability density functionMathematicsFading distributionStatisticsComputer scienceApplied mathematicsRayleigh fading

Abstract

fetched live from OpenAlex

New, exact closed-form expressions for the probability density function (PDF) and the cumulative distribution function (CDF) are derived for the instantaneous received end-to-end signal-to-noise ratio (SNR) of dual-hop amplify-and-forward (AF) relaying systems operating over mixed Nakagami-m and Rician fading links. The expressions are used to obtain exact integral solutions for the ergodic capacity and the average symbol error probability as well as an exact closed-form solution for the outage probability of dual-hop AF systems operating over mixed links. The results obtained represent the first exact results for the cases of composite Nakagami-m/Rician fading links. The exact performance metrics are compared to performance bounds in the literature, and it is shown that the performance bounds are not tight for medium ranges of SNR. The effects of fading parameters on the system performance are studied. It is shown that the limiting slopes of the average error probability and outage probability curves are not affected by the fading parameters, however, SNR gains are achieved by increasing the fading parameter. Moreover, it is shown also that an increase in the Rician parameter, K, results in a notable SNR gain in the system performance, while an increase in the Nakagami-m parameter, m, has diminishing returns and gives negligible improvement in the system performance in some instances.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.276
Teacher spread0.230 · 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 designTheoretical or conceptual
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

Citations13
Published2012
Admission routes1
Has abstractyes

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