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Record W4293863170 · doi:10.1109/siu55565.2022.9864679

Comparison of Different Intelligent Reflective Surface Designs in terms of Beam Properties at Sub-Terahertz Frequencies

2022· article· en· W4293863170 on OpenAlexaff
Ada Irem Pekdemir, Özgür Özdemir, Güneş Karabulut Kurt

Bibliographic record

Venue2022 30th Signal Processing and Communications Applications Conference (SIU) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsPolytechnique MontréalStantec (Canada)
Fundersnot available
KeywordsTerahertz radiationTerahertz gapTransmitterChannel (broadcasting)WirelessCommunications systemElectromagnetic radiationWavelengthFrequency bandOptoelectronicsOpticsElectronic engineeringComputer scienceTerahertz metamaterialsTelecommunicationsAntenna (radio)PhysicsEngineeringFar-infrared laser

Abstract

fetched live from OpenAlex

Terahertz (THz) communication is one of the remarkable topics in the field of communication. Terahertz communication, which is one of the promising topics in meeting the rapidly increasing number of devices and the need for data speed and channel capacity, aims to increase the wireless communication frequency band up to terahertz level. When the terahertz frequency is used the wavelength decreases significantly, which causes electromagnetic waves to be more affected by the channel effects. There must be a clear line of sight (LOS) between the transmitter and the receiver in THz communication systems and the electromagnetic wave sent must be highly directive. The use of intelligent reflective surfaces in the terahertz band can provide advantages in the reflected wave being more directive. In this study, different Intelligent Reflective Surface designs in the literature are implemented and a performance comparison is presented in terms of beam characteristics.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.084
GPT teacher head0.312
Teacher spread0.228 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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