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Record W2919315831 · doi:10.1109/tap.2019.2902668

Extension of Butler Matrix Number of Beams Based on Reconfigurable Couplers

2019· article· en· W2919315831 on OpenAlexaff
Kejia Ding, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsBandwidth (computing)PhysicsCrossoverExtension (predicate logic)Beam (structure)Matrix (chemical analysis)Computer scienceTopology (electrical circuits)CombinatoricsAlgorithmMathematicsOpticsTelecommunicationsMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A scheme to extend the beam number of Butler matrices (BMs) by utilizing reconfigurable couplers is presented and illustrated with experimental verification. The beam number of a traditional 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> BM can be increased to 3 . 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> by substituting reconfigurable couplers for all hybrids while maintaining the structure and other components as the original. Moreover, only N sets of different parameters are required for these couplers. The principle, properties, and the generalized expressions for the N sets of parameters are discussed and exhibited. The properties of the extended beams are illustrated in terms of beam directions and crossover levels. As an example, a switchable 12-beam forming network extended from a 4 × 4 BM for 2.4 GHz applications is fabricated and tested. Over a 30% relative bandwidth is achieved with phase errors less than ±12°, amplitude unbalances lower than ±1.7 dB, isolations better than -15.5 dB, and a return loss better than 11.5 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.404

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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

Citations33
Published2019
Admission routes1
Has abstractyes

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