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Record W2519167148 · doi:10.1049/iet-map.2016.0457

Dielectric resonator based MIMO antenna system enabling millimetre‐wave mobile devices

2016· article· en· W2519167148 on OpenAlexaff
Mohammad S. Sharawi, Symon K. Podilchak, Mohamed T. Hussain, Yahia M. M. Antar

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMillimetre waveAntenna (radio)MIMOExtremely high frequencyDielectric resonatorResonatorMillimeterElectronic engineeringTelecommunicationsAcousticsElectrical engineeringPhysicsComputer scienceOptoelectronicsEngineeringOptics

Abstract

fetched live from OpenAlex

In this study, a millimetre‐wave dielectric resonator (DR)‐based multiple‐input–multiple‐output (MIMO) antenna system based on two linear arrays is presented. Each array that represents a single MIMO antenna consists of four cylindrical DR antenna (cDRA) elements operating at 30 GHz with a bandwidth of at least 1 GHz. Each array is designed with a fixed beam direction which is tilted to provide low field correlation. A passive microstrip‐based feed network was designed to achieve this beam tilting enabling suitable magnitude and phase excitation of the individual cDRAs for radiation. The complete antenna system was designed on a two‐layer substrate occupying 48 mm × 21 mm. Excellent field correlation values were measured (below 0.002) across the band of operation while peak gains were >7 dBi. High radiation efficiency is obtained. The proposed design approach for beam tilting, which enables MIMO operation, may also be useful for other compact implementations to support 5G communications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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 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

Citations130
Published2016
Admission routes1
Has abstractyes

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