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Record W3168138217 · doi:10.1109/tap.2021.3076194

Enhanced Coverage in the Shadow Region Using Dipole Scatterers at the Corner

2021· article· en· W3168138217 on OpenAlexafffund
Roshanak Zabihi, Christopher G. Hynes, Rodney G. Vaughan

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMultipath propagationShadow mappingBandwidth (computing)Antenna gainUniform theory of diffractionShadow (psychology)Communications systemMIMOElectronic engineeringDipole antennaTelecommunicationsDiffractionOpticsAntenna aperturePhysicsAntenna (radio)EngineeringBeamforming

Abstract

fetched live from OpenAlex

Coverage in mobile communications systems requires an acceptable link gain over a maximum service area, with areas of low link gain being problematic. A standard approach to maximize link gain is to deploy multiple-input–multiple-output (MIMO) antennas that adapt to the changing multipath. There is also recent interest in using active surfaces for adaptive reflections within the multipath environments, and this calls for new ideas at the system and component levels. A critical mechanism in urban propagation is corner diffraction, which illuminates shadow areas but with low link gain. We present a simple add-on system for corners, comprising a simple scattering dipole or scattering array, which can significantly improve link gain in the shadow region over a wide bandwidth. The concept is demonstrated through analysis, simulation, and physical measurement. Only shadowed areas are boosted, and the line-of-sight coverage is not significantly affected. Our demonstration is for a passive, fixed configuration, which is suitable for retrofitting to existing systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.689
Threshold uncertainty score0.259

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 head0.025
GPT teacher head0.240
Teacher spread0.216 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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