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Record W2117235616 · doi:10.1109/tap.2010.2055783

Depth and Rate of Fading on Fixed Wireless Channels Between 200 MHz and 2 GHz in Suburban Macrocell Environments

2010· article· en· W2117235616 on OpenAlexafffund
Kyle N. Sivertsen, Anthony Liou, David G. Michelson

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2010
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of British Columbia
FundersBC Hydro
KeywordsFadingMacrocellTransmitterFadeFading distributionRadio spectrumWirelessShadow mappingTelecommunicationsComputer scienceRemote sensingEnvironmental scienceElectronic engineeringAcousticsPhysicsChannel (broadcasting)GeologyEngineeringBase stationRayleigh fading

Abstract

fetched live from OpenAlex

Various bands between 200 MHz and 2 GHz have recently been reallocated to multipoint fixed wireless services. The links in such systems are usually obstructed by buildings and foliage and are susceptible to fading caused by windblown trees and foliage. To date, there have been relatively few efforts to characterize either the depth of fading in bands below 1.9 GHz or the rate of fading in any of these bands. We transmitted CW signals in the 220, 850 and 1900 MHz bands from a transmitter located 80 m above ground level in a typical suburban macrocell environment and collected time-series of received signal strength at distances between 1 and 4 km from the site. We reduced the data to show how the depth and rate of fading depend on the frequency band, time-averaged wind speed and distance in such an environment. Our most significant finding is that the rate of signal fading is very similar in all three bands. In particular, it is not proportional to carrier frequency, as a simplistic model involving moving scatterers might suggest. These results will provide useful guidance to those who seek to simulate, or develop detailed physical models of, fade dynamics in such environments.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.583

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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 designBench or experimental
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

Citations6
Published2010
Admission routes2
Has abstractyes

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