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Dual-Beam Antenna Array Using Multilayer Feeding Technology

2024· article· en· W4402968281 on OpenAlexaff
Mostafa O. Shady, Oludayo Sokunbi, Mohamed Mamdouh M. Ali, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsInstitut National de la Recherche ScientifiqueConcordia UniversityApollo Microwaves (Canada)
Fundersnot available
KeywordsDual (grammatical number)Antenna (radio)Computer scienceBeam waveguide antennaBeam (structure)Antenna arrayAntenna measurementElectronic engineeringPeriscope antennaOptoelectronicsMaterials scienceOpticsTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

Multi-beam arrays play a crucial role in spatial diversity to enhance the reliability of wireless links and simultaneously cover multiple areas. This approach offers several advantages, such as size reduction and lower power consumption compared to single-beam antennas. In this work, a novel dual-beam radiation pattern for 5G millimeter-wave (mmWave) communications is proposed through a unique multilayer feeding structure. The key innovation lies in a 180° rotation between antennas in different layers, enabling in-phase array excitation to produce a dual-beam pattern. To validate the proposed concept, an in-phase multilayer power divider is employed to excite two end-fire antennas. Obtained results demonstrate that the proposed structure covers a substantial bandwidth of 32.25% for two beams, oriented at ±33°.

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 score0.953
Threshold uncertainty score0.395

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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