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Record W4322502968 · doi:10.3390/s23052590

Generation of Mixed-OAM-Carrying Waves Using Huygens’ Metasurface for Mm-Wave Applications

2023· article· en· W4322502968 on OpenAlexaff
Hassan Naseri, Peyman PourMohammadi, Noureddine Melouki, Fahad Ahmed, Amjad Iqbal, Tayeb A. Denidni

Bibliographic record

VenueSensors · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhysicsOpticsAngular momentumAntenna (radio)Position (finance)Phase (matter)Aperture (computer memory)Computer scienceTopology (electrical circuits)AcousticsTelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Antennas that generate orbital angular momentum (OAM) have the potential to significantly enhance the channel capacity of upcoming wireless systems. This is because different OAM modes that are excited from a shared aperture are orthogonal, which means that each mode can carry a distinct stream of data. As a result, it is possible to transmit multiple data streams at the same time and frequency using a single OAM antenna system. To achieve this, there is a need to develop antennas that can create several OAM modes. This study employs an ultrathin dual-polarized Huygens' metasurface to design a transmit array (TA) that can generate mixed-OAM modes. Two concentrically-embedded TAs are used to excite the desired modes by achieving the required phase difference according to the coordinate position of each unit cell. The prototype of the TA, which operates at 28 GHz and has a size of 11 × 11 cm 2, generates mixed OAM modes of -1 and -2 using dual-band Huygens' metasurfaces. To the best of the authors' knowledge, this is the first time that such a low-profile and dual-polarized OAM carrying mixed vortex beams has been designed using TAs. The maximum gain of the structure is 16 dBi.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.417

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 head0.110
GPT teacher head0.309
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

Citations14
Published2023
Admission routes1
Has abstractyes

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