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Record W2167721244 · doi:10.1109/twc.2012.12.120364

Lifetime Analysis of a Two-Hop Amplify-and-Forward Opportunistic Wireless Relay Network

2013· article· en· W2167721244 on OpenAlexaff
S. Ali Mousavifar, Cyril Leung

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRelayTransmitter power outputComputer scienceWirelessRelay channelMarkov chainComputer networkWireless networkEnergy (signal processing)Hop (telecommunications)Expression (computer science)Power (physics)TelecommunicationsTopology (electrical circuits)MathematicsElectrical engineeringStatisticsEngineeringPhysicsChannel (broadcasting)Transmitter

Abstract

fetched live from OpenAlex

An expression is derived for the probability mass function (PMF) of the relay transmit power in a variable gain amplify-and-forward (VG-AF) opportunistic wireless relay network (OWRN). The PMF is used to calculate the average relay transmit power. An expression is also obtained for evaluating the transition probabilities between energy states in a Markov chain model of the OWRN. This model is used to compute the average OWRN lifetime for a small number of relays, allowable transmit power levels, and low initial relay energy levels. Unfortunately, the computational complexity of this approach becomes prohibitive as the number of relays, transmit power levels, and initial energy levels increase. A low-complexity method, based on an existing expression and the average relay transmit power, is used to estimate the average network lifetime. The method is shown to yield very accurate results for practical initial relay energy levels.

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 categoriesMeta-epidemiology (narrow)
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 score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.044
GPT teacher head0.294
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 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

Citations16
Published2013
Admission routes1
Has abstractyes

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