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Record W2009686316 · doi:10.1109/lcomm.2014.2345671

Exact Outage Probability for a Wireless Diversity Network With Spatially Random Mobile Relays

2014· article· en· W2009686316 on OpenAlexafffund
Peijian Ju, Wei Song, A-Long Jin

Bibliographic record

VenueIEEE Communications Letters · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceRelayOutage probabilityNode (physics)Wireless networkComputer networkWirelessCoverage probabilityProbability distributionRandom variableTopology (electrical circuits)Quality of serviceFadingChannel (broadcasting)TelecommunicationsMathematicsStatisticsEngineering

Abstract

fetched live from OpenAlex

This work analyzes the outage probability of a wireless cooperative network with a single helper selected from spatially random mobile helpers. We consider two representative scenarios, where the mobility of mobile helpers follows random direction (RD) and random way point (RWP). Exact analytical expressions of the outage probability are derived and validated by simulations. The analysis can be used to adapt helper selection based on statistics of mobile nodes and channel conditions to satisfy a quality-of-service requirement. The outage probability of RWP is found close to that of RD for a large relay area. Hence, it is reasonable to estimate the outage probability of RWP as in RD based on the assumption of a uniform node distribution.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0040.002
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 head0.038
GPT teacher head0.259
Teacher spread0.221 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2014
Admission routes2
Has abstractyes

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