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A Compact Dual Circularly Polarized MIMO Antenna with Controlled Slots for 5G Wireless Applications Across mm-Wave Spectrum

2024· article· en· W4404036661 on OpenAlexaff
Hassan Zakeri, Gholamreza Moradi, Mohammad Alibakhshikenari, Patrizia Liveri, Tayeb A. Denidni, Sławomir Kozieł, Iyad Dayoub, Ernesto Limiti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversidad Carlos III de Madrid
KeywordsMIMOWirelessTurnstile antennaPhysicsAntenna (radio)Dual (grammatical number)Electronic engineeringComputer scienceTelecommunicationsMicrostrip antennaCoaxial antennaEngineering

Abstract

fetched live from OpenAlex

This work presents a two-port circularly polarized multiple inputs multiple outputs (MIMO) antenna proposed for 5G mm-wave applications. This antenna consists of four low profiles slotted compact circular microstrip patch antenna with a partial ground and parasitic element for signal exchange in 5G spectra at the mm-wave range. The antenna operates between 27.2 and 28.35 GHz. A Roger RT5880 dielectric substrate measuring a compact dimension of 45.6 × 14.7 × 0.508 mm3, is used to etch the slotted antenna. The patch’s cross slots are designed to improve the targeted performance metrics including a wider bandwidth and better antenna impedance matching. At the resonance frequency of 27.7 GHz, the antenna showcases a gain of 4.43dB and an impressive return-loss of -15.6 dB. Coaxial feeding is employed for its operation. The MIMO antenna array is designed to deliver high performance with an average realized gain and radiation efficiency of 9.0 dB and 90%, respectively. It has a large bandwidth of 1.15 GHz, high gain, and directivity make it a promising candidate for 5G mm-wave GHz band applications.

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 science0.0010.000
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 head0.015
GPT teacher head0.240
Teacher spread0.226 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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