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Record W2168557634 · doi:10.1109/acssc.2009.5470114

Spectrum efficient cooperative relaying based on outage-multiplexing tradeoff analysis

2009· article· en· W2168557634 on OpenAlexaff
Youngwook Ko, Sergiy A. Vorobyov, Masoud Ardakani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelayMultiplexingRelay channelComputer networkComputer scienceOutage probabilityHop (telecommunications)Power (physics)Topology (electrical circuits)FadingTelecommunicationsEngineeringElectrical engineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Spectrum efficient multiple relay selection strategy for two-hop cooperative decode-and-forward relay networks is proposed for the case when the sum power among all relay nodes is limited. Based on the outage-multiplexing tradeoff (OMT), the number of active relay nodes is maximized so that the resulting sum-relay capacity is maximized while each relay outage capacity remains greater than or equal to a certain target level. Using asymptotic analysis, it is shown that for the proposed OMT relaying strategy the associated multiplexing and cooperative system diversity gains improve proportionally with the number of active relay nodes. It is also shown analytically that the proposed OMT relaying outperforms the conventional opportunistic single relaying in terms of the sum-relay capacity.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.281
Teacher spread0.250 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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