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Record W2107363488 · doi:10.1109/lcomm.2010.07.100407

Amplify-and-Forward Relaying in Channel-Noise-Assisted Cooperative Networks with Relay Selection

2010· article· en· W2107363488 on OpenAlex
Daniel Benevides da Costa, Sonia Aı̈ssa

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Communications Letters · 2010
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsErgodic theoryRelayCumulative distribution functionProbability density functionChannel (broadcasting)Expression (computer science)Computer scienceTopology (electrical circuits)Signal-to-noise ratio (imaging)Upper and lower boundsClosed-form expressionSelection (genetic algorithm)Channel capacityNoise (video)Relay channelOutage probabilityMathematicsAlgorithmApplied mathematicsTelecommunicationsStatisticsFadingMathematical analysisPhysicsCombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

The performance of dual-hop channel-noise-assisted amplify-and-forward cooperative networks with relay selection in a clustered structure is evaluated. Exact closed-form expressions for the probability density function and cumulative distribution function of the end-to-end signal-to-noise ratio (SNR) are derived. Making using of these expressions, the average symbol error probability (SEP) and ergodic capacity are numerically evaluated. In addition, a highly accurate closed-form approximate expression for the average SEP and a closed-form upper bound for the ergodic capacity are derived. Numerical examples are plotted in order to illustrate the efficiency of our formulations.

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.

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 score0.727
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.269
Teacher spread0.241 · 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