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Record W2915588049 · doi:10.1109/access.2019.2901440

CP Antenna Array With Switching-Beam Capability Using Electromagnetic Periodic Structures for 5G Applications

2019· article· en· W2915588049 on OpenAlexafffund
Mohamad Mantash, Tayeb A. Denidni

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsAntenna arrayRadiation patternAntenna (radio)Coaxial antennaBeam steeringPhysicsComputer scienceBeam (structure)Telecommunications

Abstract

fetched live from OpenAlex

This paper proposes a novel circularly polarized electromagnetic band-gap (EBG) antenna array backed by an artificial magnetic conductor (AMC) that operates in the 26-32-GHz bands. The designed antenna is a 2 x 2 Yagi-Uda antenna array that uses the directors' semicircles to direct the radiation in the horizontal end-fire direction. The feeding of the array is designed with two parallel T-junction portions forming a parallel feeding network that converts the polarization from linear to circular. An AMC is placed underneath the antenna to suppress the backward radiation and obtain the propagation only in the +z plane. The novel approach is based on the fact that EBGs are placed around the array to give to the antenna, beamswitching capability. This simple technique provides a high beam tilting angle of +90° from the end-fire to the full broadside plane. The final EBG-AMC-antenna array is circularly polarized and presents, at 29 GHz, a gain of 11.9 dBi, and an axial ratio bandwidth of 10% from 28 to 31 GHz. By assessing the high antenna performances presented in this paper, and the high angle beam-switching capability by adding the EBG, the novel array could be seen as a potential candidate for implementation in future 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: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.619

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 head0.013
GPT teacher head0.253
Teacher spread0.239 · 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

Citations33
Published2019
Admission routes2
Has abstractyes

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