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Record W4399766951 · doi:10.1109/ojap.2024.3416399

3-D-Printed Encapsulated Dielectric Resonator Antennas With Large Operation Frequency Ratio for Future Wireless Communications

2024· article· en· W4399766951 on OpenAlexafffund
Reza Shamsaee Malfajani, Reza Damansabz, Sampada Bodkhe, Daniel Therriault, Jean‐Jacques Laurin, Mohammad S. Sharawi

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologies
KeywordsDielectric resonatorWirelessDielectric resonator antennaResonatorDielectric3d printedMaterials scienceElectrical engineeringOptoelectronicsElectronic engineeringComputer scienceTelecommunicationsEngineeringBiomedical engineering

Abstract

fetched live from OpenAlex

Shared aperture antennas are versatile structures that can fulfill the demand for multi-band compact antennas in multi-standard emerging communication systems. However, the requirement of operation at widely separated frequency bands, such as sub-6-GHz band and mm-wave band in 5G, poses a challenge. This paper introduces a novel Encapsulated Dielectric Resonator Antennas (E-DRAs) designed for operation at sub-6-GHz and mm-wave bands for 5G and beyond applications. The DRA part of the antenna consists of an array of small cylindrical DRAs (cDRA) encapsulated in a larger cylinder. At mm-wave band, the small cDRAs are radiating elements while the larger cylinder acts as a lens to enhance the gain and provide beam switching at discrete angles by switching the feed between the small cDRAs. At sub-6-GHz band, the large cylinder is the main radiator. The antenna is realized with a 3D printing process using two distinct ABS materials with different infills. Measurements of the fabricated antenna show a maximum gain of 7.8 dBi at 3.35 GHz and 19.7 dBi at 27 GHz. The measured bandwidth is 20.2% centered at 3.45 GHz and 28.7% centered at 28.5 GHz. The array of small cDRAs with five elements enables beam switching within ±30°.

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)0.001
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: none
Teacher disagreement score0.988
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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