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Performance Analysis of Pairwise Amplify-and-Forward Multi-Way Relay Networks

2012· article· en· W2055465175 on OpenAlex
Gayan Amarasuriya, Chintha Tellambura, Masoud Ardakani

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 Wireless Communications Letters · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMoment-generating functionPairwise error probabilityCumulative distribution functionRelayProbability density functionComputer sciencePairwise comparisonExpression (computer science)MultiplexingSignal-to-noise ratio (imaging)Topology (electrical circuits)Context (archaeology)Closed-form expressionInterference (communication)Random variableAlgorithmMathematicsTelecommunicationsFadingStatisticsPower (physics)Decoding methodsArtificial intelligenceChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

For the first time, the performance of pairwise amplify-and-forward multi-way relay networks (MWRNs) is studied. To this end, new end-to-end signal-to-noise ratio (e2e SNR) expression at an arbitrary source is first derived in closed-form, and thereby, an insightful statistical characterization is developed. In this context, tight closed-form approximations are derived for the cumulative distribution function, probability density function and moment generating function of the e2e SNR. Specifically, the conditional outage probability and the average bit error rate conditioned on error-free back-propagated successive interference cancellation are also derived in closed-form. Moreover, valuable insights into practical MWRN system-designing is obtained by deriving the fundamental diversity-multiplexing trade-off by employing the high SNR outage probability approximation.

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 score0.824
Threshold uncertainty score0.792

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.051
GPT teacher head0.287
Teacher spread0.235 · 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