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Record W2156668176 · doi:10.1109/twc.2009.090211

Characterization of time variation on 1.9 GHz fixed wireless channels in suburban macrocell environments

2009· article· en· W2156668176 on OpenAlexaff
Anthony Liou, Kyle N. Sivertsen, David G. Michelson

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMacrocellFadingDoppler effectEstimatorWind speedAntenna height considerationsWirelessTelecommunicationsStatisticsMathematicsAntenna (radio)Computer scienceBase stationPhysicsChannel (broadcasting)Meteorology

Abstract

fetched live from OpenAlex

We observed temporal fading on 1.9 GHz fixed wireless channels during short-term measurements at 107 different locations in a suburban macrocell environment characterized by flat terrain and heavy foliage in order to determine how the rate of fading varies with average wind speed and distance. For each location, we estimated: (1) the Ricean K-factor using a moment-based estimator and (2) an equivalent Doppler frequency which is related to the maximum Doppler frequency by a factor that depends upon the shape of the Doppler spectrum. We did so by fitting the measured level crossing rate (LCR) and average fade duration (AFD) distributions to expressions normally justified for mobile wireless links using a method recently proposed by Feick, Valenzuela and Ahumada (2007). As has been observed at other sites, the Ricean K-factor decreased with both average wind speed and distance. However, we found that the equivalent Doppler frequency was effectively uncorrelated with wind speed and noticeably increased with distance. Similar measurements at other sites will be required to determine the extent to which these trends are affected by foliage density and tower height.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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
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

Citations7
Published2009
Admission routes1
Has abstractyes

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