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Record W3114361545 · doi:10.18280/mmep.070407

A Metamaterial Loaded Microstrip Patch Antenna for Lower 5G U-NII Spectrum

2020· article· en· W3114361545 on OpenAlexvenueno aff
Afia Mubassira Islam, Emraul Islam Emon, Anis Ahmed

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2020
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsnot available
FundersUniversity of Dhaka
KeywordsPatch antennaMicrostrip antennaGround planeMetamaterial antennaMicrostripBandwidth (computing)Split-ring resonatorPhysicsAntenna (radio)MetamaterialOptoelectronicsAcousticsTelecommunicationsCoaxial antennaOpticsComputer science

Abstract

fetched live from OpenAlex

In this study, we have proposed a metamaterial loaded microstrip patch antenna for the sub-6 GHz range to operate in the Unlicensed National Information Infrastructure (U-NII) band. The Proposed Microstrip Patch Antenna (PMPA) has a U-shaped patch and an array of Complementary Split Ring Resonators (CSRR) in the ground plane. By adding a slot in the middle, the rectangular patch becomes a U-shaped one which is responsible for the enhancement of antenna bandwidth and gain. Our antenna provides a bandwidth of 392 MHz which is about 2.7 times larger compared to that of a Conventional Microstrip Patch Antenna (CMPA) of the same dimension. The maximum gain of our antenna is found 6.56 dB which is around 2 dB higher than that of the conventional one (4.72 dB). Due to the addition of the CSRR array in the ground plane, an improved impedance matching of 50 ohms has been achieved. The operating frequency range of the PMPA is from 5.525 to 5.917 GHz which can be used for 5G applications such as Wi-fi, Wi-Max, and IoT devices in the U-NII band.

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 score0.004

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 communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.191
Teacher spread0.165 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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