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Record W2766193075 · doi:10.1002/itl2.13

Outage probability for a decode‐and‐forward SWIPT relaying system in Nakagami fading

2017· article· en· W2766193075 on OpenAlexaff
Farhan Nawaz, Syed Ali Hassan, Sonia Aı̈ssa, Sajid Saleem

Bibliographic record

VenueInternet Technology Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsNakagami distributionFadingRelayMaximum power transfer theoremComputer scienceRayleigh fadingWirelessElectronic engineeringEnergy harvestingEnergy (signal processing)TelecommunicationsPower (physics)Decoding methodsEngineeringStatisticsMathematicsPhysics

Abstract

fetched live from OpenAlex

The outage probability of a decode‐and‐forward (DF) relaying system based on simultaneous wireless information and power transfer (SWIPT) in the presence of Nakagami fading is investigated. The relaying model considers both the source‐destination direct link in addition to the source‐relay‐destination link. The power splitter at the relaying device provides energy to the relay by splitting the received signal power into energy harvesting and information transfer parts. The derived outage expression is verified using simulation results. The results show an impressive amount of percentage decrease in outage probability for Nakagami fading with different values of shape parameter in comparison to Rayleigh fading.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.220
Teacher spread0.209 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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