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Record W4406921692 · doi:10.1109/ojcoms.2025.3536316

A Compact 12-Port MIMO Dielectric Lens Antenna With Wide-Angle Beam Scanning for Ka-Band Applications

2025· article· en· W4406921692 on OpenAlexaff
Massinissa Belazzoug, Idris Messaoudene, Yassine Himeur, Boualem Hammache, Salem Titouni, Rida Gadhafi, Raouf Zerrougui, Youcef Braham Chaouche, Shadi Atalla, Wathiq Mansoor

Bibliographic record

VenueIEEE Open Journal of the Communications Society · 2025
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsKa bandOpticsAntenna (radio)Lens (geology)DielectricPort (circuit theory)OptoelectronicsMaterials sciencePhysicsTelecommunicationsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper tackles significant challenges in the design of millimeter-wave MIMO (Multi-input Multi-output) antennas for the Ka-band (26-30 GHz) as utilized in 5G applications. Current antenna designs suffer from limitations such as constrained bandwidth, inadequate gain, and restricted beam scanning capabilities, adversely affecting performance, particularly in high-density urban environments. A novel MIMO antenna featuring a 12-port dielectric lens has been proposed to address these issues, introducing several advancements to improve key performance metrics. The antenna demonstrates a peak realized gain of approximately 14 dBi and achieves a bandwidth exceeding 4 GHz. Importantly, it features full 360° beam scanning capabilities and maintains an exceptionally low envelope correlation coefficient (ECC) of less than 0.0014. The design also shows impressive radiation efficiency consistently exceeding 95%, complemented by excellent isolation characteristics with mutual coupling maintained below −30 dB. This research provides a scalable solution that could significantly enhance the performance of next-generation wireless networks, addressing the high demands of urban 5G deployments.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.036
GPT teacher head0.287
Teacher spread0.251 · 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 designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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