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Record W3016623501 · doi:10.1109/access.2020.2988000

Ultra-Wideband Differential Fed Hybrid Antenna With High-Cross Polarization Discrimination for Millimeter Wave Applications

2020· article· en· W3016623501 on OpenAlexaff
Magid Alzidani, Islam Afifi, Muftah Asaadi, Abdel-Razik Sebak

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsExtremely high frequencyWidebandPolarization (electrochemistry)OpticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

A wideband differential fed patch antenna with high cross-polarization discrimination is proposed at mm-wave range. For the purpose of increasing antenna bandwidth, capacitive coupling technique is used. Also, the differential feeding is utilized to ensure broadside radiation and low cross-polarization. The designed antenna has an ultra-wide bandwidth of 55% around 30 GHz with S11 ≤ -10 dB, and a peak gain of 8 dBi. The radiation pattern has a cross polarization level less than -20 dB over the operating frequency band. The differential feeding technique depends on equal power division and 180° phase difference for all the antenna bandwidth. Due to the wide bandwidth of the differential feeding antenna element, two designs of the feeding circuits (which include rat race and probe strip line transition) are used to cover the whole frequency band of the antenna. A gain enhancement has been achieved by adding a horn to the designed antenna with an efficient aperture efficiency. The designed antennas have fractional bandwidths of 28.73% (at center frequency 25.64GHz) and 26.3% (at the center frequency 32.2 GHz), for the lower and the upper bandwidths, respectively. An average gain of 14.5 dBi has been achieved for the frequency band from 21.8 GHz to 36.5 GHz. The antenna performance is verified through fabrication and measurement, where the simulated and measured results are in a good agreement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score0.657

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 head0.021
GPT teacher head0.242
Teacher spread0.222 · 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 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

Citations15
Published2020
Admission routes1
Has abstractyes

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