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Record W2109737953 · doi:10.1109/pacrim.1993.407294

Empirical assessment of 1.7 GHz micro-diversity in various buildings

2002· article· en· W2109737953 on OpenAlexaff
S.R. Todd, M. El-Tanany, S. Mahmoud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsCarleton University
FundersInstituto de Telecomunicações
KeywordsFadingDiversity schemeMultipath propagationAntenna diversityStatisticsDiversity (politics)Computer scienceCumulative distribution functionEnvelope (radar)TelecommunicationsEnvironmental scienceRemote sensingElectronic engineeringMathematicsEngineeringGeographyProbability density functionAntenna (radio)

Abstract

fetched live from OpenAlex

To achieve reliable radio communications inside buildings, microdiversity is required to mitigate the severe multipath and temporal fading. The authors present results of diversity measurements from 100 locations in four university buildings. They computed cumulative distribution functions (CDFs) of the envelope fading statistics for space, frequency, and space/frequency (hybrid) diversity. CDFs from space diversity were best in open areas with an average value of -7.6 dB at 99% time availability and worst in a building with cement interior walls yielding -8.9 dB. These differences were only 1 to 2 dB, suggesting that these results may be useful for similar buildings. Frequency diversity showed similar trends but with 1.5 to 2.0 dB poorer results. In general, the output fading statistics were best in areas with many line-of-sight (LOS) paths, while the diversity gains were larger for buildings with more severe fading due to non-LOS paths. Results from one-minute measurements indicated that higher orders of diversity significantly reduce the fading variations between locations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.620

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 head0.059
GPT teacher head0.276
Teacher spread0.217 · 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
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

Citations1
Published2002
Admission routes1
Has abstractyes

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