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Record W4413854738 · doi:10.31272/jeasd.3077

Ultra-Wideband Antenna Array for Modern Millimeter-Wave Wireless Applications

2025· article· en· W4413854738 on OpenAlexaff
Hussam Al‐Saedi, Muhannad Y. Muhsin, Zainab Faydhe Al-Azzawi, Wael M. Abdel‐Wahab

Bibliographic record

VenueJournal of Engineering and Sustainable Development · 2025
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExtremely high frequencyWidebandWirelessAntenna (radio)TelecommunicationsComputer scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

An ultra-wideband antenna with high gain performance for future 5G and beyond millimeter-wave (mm-wave) applications is presented in this paper. The proposed microstrip patch antenna element was designed based on an aperture-coupled microstrip patch with four loaded parasitic patches to improve the frequency bandwidth. The antenna was built by employing four metal layers. Roger RT/duroid 6006 and RT/duroid 6002 were used in this design as feeding and antenna substrates, respectively. The suggested antenna element was utilized to construct a 2×2 antenna array fed with a corporate feeding network to achieve high gain and desired radiation characteristics. ANSYSEDT 2022 R1 full-wave simulator was used to design and analyze the proposed antenna and the corporate feeding network. The array gain is between 9.5 dB and 13.2 dB over the desired band of interest. The attained reflection coefficient (S11) of better than -10 dB is in the range of 26.15 GHz – 38.6 GHz, occupying a relatively compact antenna size. Moreover, the presented antenna realizes a low cross-polarization and a good front-to-back ratio. The obtained results show that the presented antenna can be considered an excellent candidate for modern wireless communication systems in mm-wave wideband 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 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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.566

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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
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

Citations2
Published2025
Admission routes1
Has abstractyes

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