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Record W4383376470 · doi:10.1029/2022rs007623

A Novel Low‐Loss Planar PRGW Crossover Design for 5G Applications

2023· article· en· W4383376470 on OpenAlexafffund
Zahra Mousavirazi, Mohamed Mamdouh M. Ali, Pejman Rezaei, Abdel-Razik Sebak, Tayeb A. Denidni

Bibliographic record

VenueRadio Science · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia UniversityInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersInstitut national de la recherche scientifique
KeywordsCrossoverPlanarBandwidth (computing)Return lossInsertion lossExtremely high frequencyMillimeterComputer scienceOpticsMaterials scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Abstract In this paper, a planar 0‐dB crossover using printed ridge gap waveguide (PRGW) technology is proposed and designed for millimeter‐wave applications. PRGW as a quasi‐TE mode is considered as the modern guiding technology at high frequencies and for the 5G and other upcoming communications. A prototype of the proposed PRGW crossover working around 30 GHz is fabricated and measured to validate the simulated results. Comparing simulated with measured results, a good agreement is observed. The measured results demonstrate that the designed crossover offers an insertion loss of better than 0.5 dB over the whole operating frequency bandwidth from 29 to 31 GHz where the return loss level is better than 15 dB and the isolation level is less than −13 dB. Compared with the other reported crossover structures, the designed PRGW crossover features a wider bandwidth with a smaller and planar configuration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2023
Admission routes2
Has abstractyes

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