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Record W2944955202 · doi:10.1109/lawp.2019.2918154

Optimum Design of a Beam-Forming Array of S-Shaped DRA Elements With a Superstrate on an SIW Feed for 5G Mobile Systems

2019· article· en· W2944955202 on OpenAlexaff
Batul Bahreini, Homayoon Oraizi, Narges Noori, Pedram Mousavi

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBandwidth (computing)ResonatorAntenna arrayPhysicsComputer scienceTopology (electrical circuits)Electronic engineeringAntenna (radio)OpticsEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

In this letter, an array of dielectric resonator antennas (DRAs) is proposed for the fifth-generation mobile network. A novel S-shaped DRA is presented as array elements. The proposed configuration is achieved by making two symmetrical segments on a conventional rectangular DRA. The results show that in this configuration the higher order modes of antenna are excited, and therefore, the 3 dB gain bandwidth and impedance bandwidth of antenna are increased. The volume of the proposed DRA without ground plane is 0.38 λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The proposed DRA provides maximum bandwidth and gain per unit volume compared to similar designs in the literature. Furthermore, the benefit of a mutual coupling (MC) compensation approach in a linear DRA array is investigated. The results show that the accuracy of a computed MC matrix is better at the resonance frequency relative to other frequencies. Also, the performance of a beam-forming algorithm is improved by using this matrix for compensating the MC effects. The proposed array is fabricated and its characteristics are measured. A very good agreement between the numerical and measured results is obtained.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.636

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 head0.013
GPT teacher head0.211
Teacher spread0.199 · 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
Published2019
Admission routes1
Has abstractyes

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