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Record W3201603786 · doi:10.21203/rs.3.rs-883060/v1

Patches of Dual Functions for Wideband MIMO Array Antenna with Linear or Circular Polarization Characteristics

2021· preprint· en· W3201603786 on OpenAlexaff
Reza Zaker, Mohsen Khalily, Rahim Tafazolli, Ahmed A. Kishk

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsWidebandMIMOAntenna arrayArray gainGround planePhysicsAntenna (radio)Collinear antenna arrayRadiation patternOpticsComputer scienceElectronic engineeringAcousticsAntenna efficiencyEngineeringTelecommunicationsBeamforming

Abstract

fetched live from OpenAlex

Abstract In this paper, a design of a monopole-based four-element MIMO array antenna is proposed. The design is based on a novel technique that makes a patch be a ground plane of the next patch. Thus, each patch has a dual function. This method is named the sharing technique. Thus, for the first time, two of such antennas can be merged, providing a subminiature structure. The method is introduced step by step. Then, a 2×2 MIMO array with a total area of 49×49 mm 2 is designed, which provides a miniaturized of 57% (from 0.18λ 2 0 to 0.076λ 2 0 @ 1.7GHz). Two linearly-polarized array samples are evaluated. One sample with center-fed patches and another with off-center-fed. Both samples provide a semi-end-fire pattern with a minimum front-to-back (F/B) ratio of 11dB and 360° rotation capability with wide 10-dB S 11 bandwidths over 100%. Moreover, an ultra-wideband circularly polarized array with broadside radiation can be achieved by simultaneous, sequential exciting all ports. The antenna achieves isolation better than 15dB, peak efficiency of 95%, and 5.9dBi gain verified with different measurements.

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)0.0010.000
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)0.0010.001

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 head0.052
GPT teacher head0.309
Teacher spread0.257 · 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
Published2021
Admission routes1
Has abstractyes

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