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Record W1983767698 · doi:10.1109/vtcfall.2012.6399349

Exact Error Analysis of Dual-Hop Fixed-Gain AF Relaying over Arbitrary Nakagami-m Fading

2012· article· en· W1983767698 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
KeywordsNakagami distributionFadingComputer scienceFading distributionAlgorithmHop (telecommunications)MathematicsTopology (electrical circuits)StatisticsTelecommunicationsCombinatoricsDecoding methods

Abstract

fetched live from OpenAlex

Closed-form error analysis of dual-hop fixed-gain amplify-and-forward (AF) relaying systems for transmissions over Nakagami fading channels has, so far, been tractable only with integer fading parameters. For the general Nakagami-m fading with arbitrary m values, the exact closed-form error analysis is more challenging. This paper goes toward solving this problem by deriving a unified error analysis framework that embraces several general modulation schemes. The obtained Error Probability (EP) expressions involve common functions which can be efficiently evaluated using standard numerical softwares. This work represents a significant improvement on previous contributions, not only by unifying the error probability expressions pertaining to dual-hop fixed gain relaying over integer Nakagami-m fading, but also by extending them to the general arbitrary Nakagami-m fading. Simulation results sustaining our analysis are provided, and the impacts of various parameters on the overall AF relaying system performance are investigated.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.049
GPT teacher head0.309
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 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

Citations8
Published2012
Admission routes1
Has abstractyes

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