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Record W4392825131 · doi:10.1109/access.2024.3375811

Wideband Endfire Antenna Array for 5G mmWave Mobile Terminals

2024· article· en· W4392825131 on OpenAlexaff
Ali Zidour, Mouloud Ayad, Mohammad Alibakhshikenari, Chan Hwang See, Yingxin Lai, Yue Ma, Boumediene Guenad, Patrizia Livreri, Salahuddin Khan, Giovanni Pau, Tayeb A. Denidni

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversidad Carlos III de MadridChinese Academy of SciencesKing Saud UniversityEuropean Commission
KeywordsWidebandComputer scienceAntenna (radio)Antenna arrayElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, a compact endfire antenna array with low-profile, small clearance, and wideband operation is proposed for millimeter-wave (mmWave) fifth-generation (5G) mobile terminals. The wideband operation is achieved by exciting two identical bow-tie dipoles inserted on both sides of a multilayer substrate fed by an asymmetric open-end stripline to slotline transition. The antenna performance is significantly improved by introducing a set of vertical metallic vias. The proposed antenna element can achieve 29 % from 24.2 GHz to 32.4 GHz with a peak realized gain that varies from 3.5 dBi to 4.5 dBi. A linear 4-element antenna array is arranged and fabricated to verify the proposed antenna beamforming capabilities. The simulated and measured bandwidth achieves a wide range of 34.4 % (24-34 GHz) to support 26, 28, and 30 GHz 5G mmWave bands with an isolation level better than 20 dB and a peak realized gain over the interested bands ranging from 7.56 to 8.14 dBi. The simulated array scanning angle is ± 68° at 28 GHz within 3-dB gain deterioration. Furthermore, the simulated spherical coverage has met the requirements of 3GPP standards which make the proposed antenna array a promising candidate to be integrated within mmWave 5G mobile devices.

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 score0.941
Threshold uncertainty score0.565

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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

Citations19
Published2024
Admission routes1
Has abstractyes

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