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Record W1990796670 · doi:10.1109/wcnc.2010.5506178

On the Approximation of the PDF of the Sum of Independent Generalized-K RVs by Another Generalized-K PDF with Applications to Distributed Antenna Systems

2010· article· en· W1990796670 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsCarleton University
Fundersnot available
KeywordsProbability density functionFadingClutterLog-normal distributionApplied mathematicsRandom variableMoment-generating functionAntenna (radio)Computer scienceRadarMathematicsWirelessMoment (physics)AlgorithmMathematical optimizationTelecommunicationsStatisticsPhysics

Abstract

fetched live from OpenAlex

The generalized-K (Gamma-Gamma) composite fading model has been proposed in the past to represent the statistics of the instantaneous power of target and clutter scattering in radar systems. Recently, this model was used in wireless communications as an alternative to the analytically intractable lognormal-based composite fading models. In this paper, a simple approach is proposed to approximate the probability density function of the sum of independent generalized-K random variables by another generalized-K one using the moment matching method. The numerical evaluations show that the proposed approximation of the sum distribution is sufficiently accurate for realistic scenarios in wireless channels. Furthermore, the applications of the obtained closed-form expressions to the performance analysis of distributed antenna systems are presented.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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

Quick stats

Citations22
Published2010
Admission routes1
Has abstractyes

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