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Record W3015472754 · doi:10.1109/access.2020.2986716

Angular Dispersion of a Scattered Underground Wireless Channel at 60 GHz

2020· article· en· W3015472754 on OpenAlexafffund
Shah Ahsanuzzaman Md Tariq, Charles Despins, Sofiène Affes, Chahé Nerguizian

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsInstitut National de la Recherche ScientifiqueÉcole de Technologie SupérieureUniversité de MontréalUniversité du Québec à MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWirelessDispersion (optics)Channel (broadcasting)OptoelectronicsOpticsMaterials scienceTelecommunicationsComputer sciencePhysics

Abstract

fetched live from OpenAlex

The 60 GHz frequency band is identified as a suitable band for Gbps speed wireless communication in an Underground mine due to its high antenna directivity and high signal attenuation. However, the rough mine surface and the 5 mm wavelength may produce rich scattering phenomenon of multipath signals. To characterize the channel and more insight into the scattering, the angular dispersion measurements are conducted in different mine gallery depths and dimensions. The scattering is analyzed by the angle of arrivals of the incoming paths at the receiver, which is characterized by the statistical parameters of the multipath shape factors. The results of the multipath shape factor are explained and show that at around 3 m link distance, the incoming paths are mostly in two or three directions within a resolution angle of around between ±30° and ±40°. The statistical distribution of the multipath angle of arrivals follows a Gaussian probability distribution. The results also show that the angular spreads of multipath are proportional to the gallery dimensions and inversely proportional to the link distances.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.254
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2020
Admission routes2
Has abstractyes

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