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Record W2157431296 · doi:10.1109/wncmf.1994.529478

Channel statistics analysis using a ray based approach

2002· article· en· W2157431296 on OpenAlexaff
David Laurenson, Stephen McLaughlin, A.U.H. Sheikh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsCarleton University
Fundersnot available
KeywordsNarrowbandNakagami distributionComputer scienceChannel (broadcasting)Rayleigh distributionTask (project management)Rayleigh scatteringProcess (computing)Probability distributionBasis (linear algebra)Statistical analysisElectronic engineeringAlgorithmStatisticsTelecommunicationsFadingMathematicsEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Narrowband indoor communication channels can be characterised by statistical distributions. The Rayleigh and Ricean distributions are commonly used to describe measured channels, but in some instances, other distributions may be found to be more appropriate for the task. Determining the reason for one particular distribution being more appropriate for one environment over another may be a non-trivial task, In order to assist in this process, a channel simulation based on the physical structure of the environment is presented, along with narrowband results, obtained both from measurement and simulation experiments. Using these results, the basis for a Nakagami distributed channel, observed in some indoor communication environments, is be shown.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.836

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)0.0010.000

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 head0.060
GPT teacher head0.224
Teacher spread0.164 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2002
Admission routes1
Has abstractyes

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