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Record W3011388893 · doi:10.1109/tap.2020.2980252

Highly Efficient 30 GHz 2x2 Beamformer Based on Rectangular Air-Filled Coaxial Line

2020· article· en· W3011388893 on OpenAlexaff
M. Akbari, Mohammadmahdi Farahani, Saman Zarbakhsh, Mansoor Dashti Ardakani, Abdel-Razik Sebak, Tayeb A. Denidni, Omar M. Ramahi

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia UniversityInstitut National de la Recherche ScientifiqueUniversity of Waterloo
Fundersnot available
KeywordsCoaxialAzimuthBroadbandOpticsAcousticsBroadsideBandwidth (computing)Antenna (radio)Transmission lineRadiation patternWaveguideTrue time delayMaterials sciencePhysicsComputer scienceTelecommunicationsPhased array

Abstract

fetched live from OpenAlex

This article introduces a low loss beamformer with a capability of 2-D scanning in elevation and azimuth directions at 30 GHz for 5G applications. The proposed beamformer is based on the broadband and highly efficient rectangular air-filled coaxial line. The beamformer is fed by four standard waveguides (WR-28) as the input ports. Correspondingly, each of the input ports using specific transitions is connected to the coaxial beamformer network. Ultimately, the coaxial transmission lines, in the radiating part of the beamformer, feed a 2 × 2 waveguide antenna array. The structure is aptly called a waveguide-coaxial-waveguide beamformer. The total dimensions of the fabricated prototype are 60 mm × 60 mm (corresponding to 6λ × 6λ). The beamformer is able to generate four fixed beams, one in each quadrant at an elevation angle of 25° from the broadside to the array axis. The results demonstrate that the proposed passive beamformer has a radiation efficiency greater than 90% over the frequency bandwidth (BW) of interest. Good stability of the radiation pattern and maximum gain over the desired BW were achieved while maintaining the sidelobe level of less than 20 dB.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.763

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 head0.011
GPT teacher head0.199
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations25
Published2020
Admission routes1
Has abstractyes

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