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Record W1997731677 · doi:10.1109/wcnc.2010.5506419

Closed-Form Error Analysis of Dual-Hop Relaying Systems over Nakagami-m Fading Channels

2010· article· en· W1997731677 on OpenAlexaff
Imène Trigui, Sofiène Affes, Alex Stéphenne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFadingNakagami distributionHypergeometric functionChannel state informationConfluent hypergeometric functionMathematicsFading distributionMoment-generating functionAlgorithmComputer scienceTopology (electrical circuits)StatisticsTelecommunicationsWirelessProbability density functionCombinatoricsDecoding methodsRayleigh fadingMathematical analysis

Abstract

fetched live from OpenAlex

In this paper we investigate the end-to-end performance of dual-hop relaying systems over non identical-Nakagami-m fading channels. Our analysis considers channel state information (CSI-) assisted relays that just amplify and retransmit the information signal also known as "non-regenerative" relays. New closed-form expressions for the average bit error probability (ABEP) are derived. The proposed expressions apply to general operating scenarios with distinct Nakagami-m fading parameters and average signal to noise ratios (SNRs) between the hops. When the fading parameter is an odd multiple of one half, the ABEP is expressed in terms of hypergeometric functions. When m takes any real non integer value, the obtained results involve the fourth Appell's hypergeometric function. For an arbitrary fading parameter, an analysis of such a scheme is performed using the well known moment-based approach.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.300
Teacher spread0.259 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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