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Record W4366446572 · doi:10.1038/s44172-023-00067-2

Conformal leaky-wave antennas for wireless terahertz communications

2023· article· en· W4366446572 on OpenAlexfundno aff
Hichem Guerboukha, Rabi Shrestha, Joshua Neronha, Zhaoji Fang, Daniel M. Mittleman

Bibliographic record

VenueCommunications Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersDivision of Computer and Network SystemsAir Force Office of Scientific ResearchFonds de recherche du Québec – Nature et technologiesNational Science Foundation
KeywordsTerahertz radiationConformal antennaWirelessConformal mapAntenna (radio)Transmission (telecommunications)Computer scienceDirectional antennaSmart antennaPhysicsRange (aeronautics)Wireless transmissionWavelengthSlot antennaElectronic engineeringOpticsTelecommunicationsEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract Future generations of wireless systems are expected to combine the use of high-frequency bands (the terahertz range) with smart interconnected devices (the Internet of Things). To realize this ambitious merging, systems will require antennas that can be mounted on nonplanar objects while generating highly directional beams. Here, we study conformal THz leaky-wave antennas at THz frequencies. We find a rich set of behaviors accessible at THz frequencies dictated by the interplay among the geometrical parameters and the wavelength. We develop simple models to describe the relevant physics, which we verify by an experimental implementation. We also demonstrate data transmission using a conformal THz antenna that can generate multiple high-gain beams with low bit error rates for increased coverage of THz wireless links.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.259
Teacher spread0.210 · 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".

Quick stats

Citations20
Published2023
Admission routes1
Has abstractyes

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