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Design and analysis of a Sub-6 GHz antenna array with high gain for 5G mobile phone applications

2024· article· en· W4403107074 on OpenAlexaff
Abderrahim Bellekhiri, Noha Chahboun, Jamal Zbitou, Aziz Oukaira, Yassin Laaziz

Bibliographic record

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsAntenna (radio)Impedance matchingAntenna arrayRadiation patternAcousticsComputer scienceAntenna gainMicrowaveMobile phoneWirelessElectrical engineeringAntenna efficiencyElectronic engineeringPhysicsElectrical impedanceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, we designed, analyzed, and simulated a 32-element antenna array for the sub-6 GHz band. Each radiating element is a square patch on a Rogers RT5880 substrate, featuring a relative dielectric permittivity of 2.2, a low-loss tangent of 0.0009, and a thickness of 0.508 mm, measuring 28.1×28.1 mm². Simulations were conducted using two electromagnetic solvers, advanced design system (ADS) and CST Microwave Studio, providing a comprehensive comparison of the results. To achieve a high balance between performance and antenna size, two 4×8 array antenna structures were designed. The simulations demonstrated excellent input impedance matching around 3.5 GHz for both configurations, with high gains of 20.5 dBi for the first and 18 dBi for the second configuration, along with directional radiation patterns. The dimensions were 315×576×0.578 mm³ for the first configuration and 170×961×0.578 mm³ for the second. These performance metrics make the proposed antenna arrays highly suitable for wireless communication technologies operating below 6 GHz, particularly for 5G mobile communications.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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