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Record W1978609099 · doi:10.1109/wcnc.2010.5506230

Symbol Error Probability Analysis for Multihop Relaying over Nakagami Fading Channels

2010· article· en· W1978609099 on OpenAlexaff
Vahid Asghari, Amine Maaref, Sonia Aı̈ssa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsAlgorithmComputer scienceStatisticsCombinatoricsTopology (electrical circuits)MathematicsDiscrete mathematics

Abstract

fetched live from OpenAlex

In this paper, we derive closed-form expressions for the average symbol error probability (SEP) of arbitrary rectangular I × J-ary quadrature amplitude modulation (QAM) in cooperative amplify-and-forward (A&F) relaying systems, when no direct line-of-sight exists between the source and the destination nodes and when the links between the K successive nodes forming the multihop cooperation chain (including the source and the destination nodes) follow independent but not-necessarily identical Nakagami-m fading distributions with arbitrary real indexes {mk}k=1Knot less than 1/2 and arbitrary average power levels {γ̅k}k=1K. The average SEP of rectangular QAM for this set-up is provided in closed-form as a linear combination of the first Lauricella's multivariate hypergeometric function, FA(K+1), K being the number of multihop links, which can be efficiently evaluated using standard numerical softwares. Simulation results sustaining our analysis are provided, and the impacts of various parameters on the overall multihop 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 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)0.013
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
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)0.0030.001

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 head0.067
GPT teacher head0.321
Teacher spread0.254 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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