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Record W4396877882 · doi:10.1109/access.2024.3399814

Singly-Fed Large Frequency Ratio Composite Dielectric Resonator Antenna for sub-6 GHz and mm-Wave 5G Applications

2024· article· en· W4396877882 on OpenAlexaff
Muhammad U. Khan, Awab Muhammad, Mohammad S. Sharawi, Moath Alathbah

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
FundersKing Saud University
KeywordsDielectricDielectric resonator antennaPermittivityMaterials scienceAntenna (radio)Resonance (particle physics)Radiator (engine cooling)OpticsOptoelectronicsPlanarComposite numberResonatorRadiationDielectric resonatorPhysicsTelecommunicationsAtomic physicsEngineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

A dielectric resonator antenna (DRA) is proposed for dual-band resonance for the mid-band (sub-6 GHz) and the high-band (mm-wave) 5G applications. The proposed composite DRA consists of an annular dielectric radiator concentrically integrated with a high-permittivity cylindrical DRA to realize resonance at two distinct frequencies with large frequency ratio. The impact of composition of two distinct dielectric materials on the radiation in both bands is analyzed taking into account the effective permittivity of the composite structure. The composite DRA is excited with single Co-planar Waveguide (CPW). The proposed DRA offers resonance at 4.44 GHz in the mid-band and 27.92 GHz in the high-band. The radiation characteristics demonstrate broadside radiation pattern having 6.8 dBi and 4.3 dBi of gain in both bands respectively. The efficiency of the DRA is observed to be 87.1% and 84.71% in the sub-6 GHz and mm-wave bands, respectively. The radiation characteristics make the proposed composite DRA a potential design for numerous 5G applications.

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 score0.976
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.263
Teacher spread0.242 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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