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Record W2031910502 · doi:10.1109/atc.2010.5672710

Performance of decode-and-forward cooperative relaying over Rayleigh fading channels with impulsive noise

2010· article· en· W2031910502 on OpenAlexaff
Khuong Ho Van, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcGill University
Fundersnot available
KeywordsRayleigh fadingQAMQuadrature amplitude modulationImpulse noiseFadingUpper and lower boundsElectronic engineeringComputer scienceGaussian noiseRelayControl theory (sociology)TelecommunicationsMathematicsAlgorithmPower (physics)PhysicsBit error rateEngineeringDecoding methodsMathematical analysis

Abstract

fetched live from OpenAlex

This paper presents the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme using quadrature amplitude modulation (QAM) in the presence of Rayleigh fading and Bernoulli-Gaussian impulsive noise. The exact symbol error probability (SEP) expression for direct transmission (DT) and SEP lower bound for DF-CR are first derived and then used to establish the optimum power allocation (OPA) for the source and the relay by exhaustive search. Analytical and simulation results for various scenarios with DT and DF-CR under the same bandwidth efficiency and power consumption are in good agreement and indicate that the lower bound SEP is very tight for the optimal Bayes receiver and CR in an impulsive noise environment can be beneficial at a certain degree depending on impulse power and impulse rate. Furthermore, OPA brings a negligible performance improvement as compared to equal power allocation under investigated conditions.

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)0.000
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: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.251
Teacher spread0.236 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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