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Record W2903863713 · doi:10.1109/antem.2018.8572980

End-Launch Horn Antenna Array for Ka-band 5G Applications

2018· article· en· W2903863713 on OpenAlexaff
Nadeem Ashraf, Abdel-Razik Sebak, Ahmed A. Kishk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsKa bandFrench hornHorn antennaDirectional antennaAntenna (radio)Computer scienceGeologyTelecommunicationsPhysicsElectrical engineeringAcousticsEngineeringSlot antenna

Abstract

fetched live from OpenAlex

In a large scale antenna system for 5G beamforming, the antenna modules can be designed as end-launch components. This concept is demonstrated by adopting half-height-pin gap waveguide technology to design a 4-element linear array. A quasi-TEM horn antenna is realized by flaring the double ridge that is surrounded by the half-height-pin electromagnetic band gap (EBG) structure. The single antenna element shows an impedance bandwidth (-10 dB) of 35% at 30 GHz with the realized gain of 7 dBi. The same impedance bandwidth is maintained with a 4-element linear array having a gain of 13.6 dBi at 30 GHz. The quasi-TEM double ridge gap waveguide (RGW) antenna is excited through a planar microstrip transition. The microstrip-to-ridge transition is designed and optimized to achieve better impedance match. The overall antenna array performance in the operating frequency range, 27.5~38 GHz, is stable with radiation efficiency around 78 %.

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: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.464

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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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