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Record W4362558901 · doi:10.3390/electronics12071707

An Advanced Array Configuration Antenna Based on Mutual Coupling Reduction

2023· article· en· W4362558901 on OpenAlexaff
Salaheddine Aourik, Ahmed Errkik, Aziz Oukaira, Dhaou Said, Jamal Zbitou, Ahmed Lakhssassi

Bibliographic record

VenueElectronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité de SherbrookeUniversité du Québec en Outaouais
Fundersnot available
KeywordsMicrostrip antennaMicrostripGround planePatch antennaAcousticsAntenna (radio)ConductorElectronic engineeringMicrowaveElectrical engineeringEngineeringPhysicsTelecommunicationsMaterials science

Abstract

fetched live from OpenAlex

In this work, a microstrip antenna array that consists of 16 elements is designed at a frequency of 28 GHz, with a dimension of 35 mm × 33.5 mm and an operational bandwidth of 27.7–28.3 GHz. The idea is putting two antenna arrays next to each other on the same substrate and ground plane, as well as minimizing the overall size. This work presents a mutual coupling reduction for two arrays and studies the performance of the antennas through the suppression of surface wave propagation in a given frequency range. It proposed a new configuration for an artificial magnetic conductor (AMC) created by microstrip technology placed at a closed distance (0.3λ0) between the two antenna arrays. The improvement in the isolation between the two adjacent antenna arrays was studied according to the H-plane with a periodic unit cell of the AMC. The mutual coupling reduces to −40.0 dB at the operation frequency and isolation of 17 dB. The antenna array is also characterized by a good envelope correlation coefficient (ECC). The CST Microwave Studio electromagnetic solver was used to design, improve, and miniaturize the proposed configuration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes1
Has abstractyes

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