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Dual-Polarized OAM Antenna with Frequency and Mode Agility for Intelligent OAM Communications

2024· article· en· W4395662312 on OpenAlexaff
Hassan Naseri, Peyman PourMohammadi, Noureddine Melouki, Fahad Ahmed, Amjad Iqbal, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsReconfigurabilityMIMOAntenna (radio)Angular momentumComputer scienceElectronic engineeringAntenna arrayReconfigurable antennaMode (computer interface)TelecommunicationsPhysicsEngineeringDipole antennaAntenna efficiencyBeamforming

Abstract

fetched live from OpenAlex

A new dual-polarized Orbital Angular Momentum antenna is proposed with the capability of mode and frequency reconfigurability to pave the way for designing smarter OAM communication systems. At the first step, a dual-polarized Multiple-Input Multiple-Output (MIMO) antenna is introduced. Later, a 2×2 Uniform Circular Array (UCA) is considered using the mentioned MIMO antennas. Since each MIMO antenna owns two input ports, two 2×2 sub-UCA is obtained having orthogonal polarizations. Reconfigurable feeding networks can produce the necessary phase gradients for generating OAM modes +1 and -1 in each sub-UCA. Frequency-tunability of the structure is achieved due to the frequency-adjustability of MIMO antennas as well. According to the simulated results, the frequency tuning range is from 3.8 GHz up to 4.4 GHz, preserving the OAM characteristics. Maximum gains over 6.75 dBi are achieved over the band. Thus, the proposed systems, with tunability on mode and frequency, and having dual-polarized feature, is the new candidate for future intelligent OAM communications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · 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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.290
Teacher spread0.270 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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