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Modeling and simulation of an antenna with optimized AMC reflecting layer for gain and front-to-back ratio enhancement for 5G applications

2020· article· en· W3033296234 on OpenAlexaff
Abdesselam Hocini, Noureddine Melouki, Tayeb A. Denidni

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMATLABAntenna gainAntenna (radio)Computer scienceSide lobeMicrostrip antennaElectronic engineeringAcousticsSimulationEngineeringTelecommunicationsAntenna factorPhysics

Abstract

fetched live from OpenAlex

Abstract A low-profiled microstrip patch antenna for application in the 5G wireless communication systems is backed by a reflecting layer based on an optimized artificial magnetic conductor (AMC) to enhance the gain and the front-to-back ratio. The design and analyses process were carried out using the full-wave commercial simulator CST Microwave Studio in parallel with Matlab, using the embedded CST to Matlab VBA-based interface to create an automated simulation environment and to design both a conventional antenna and the proposed one. A genetic algorithm (GA) is used to optimize the AMC reflecting layer to achieve maximum gain and front-to-back ratio around the frequency band of interest. The results yield an important enhancement in the peak gain and front-to-back ratio, alongside a low side-lobe level (SLL) due to the successful surface waves suppression, thus making this antenna design a good candidate for future wireless communication systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.292

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 head0.057
GPT teacher head0.294
Teacher spread0.236 · 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 designSimulation or modeling
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

Citations17
Published2020
Admission routes1
Has abstractyes

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