MétaCan
Menu
Back to cohort
Record W3185730575 · doi:10.1109/siu53274.2021.9477884

Accurate Modelling of Reconfigurable Intelligent Surfaces in THz Band

2021· article· en· W3185730575 on OpenAlexaff
Burak Ahmet Celebi, Irem Aras, Kaan Emre Ozcelik, Kürşat Tekbıyık, Güneş Karabulut Kurt, Özgür Özdemir, Ali Rıza Ekti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTerahertz radiationReflection (computer programming)Phase (matter)AmplitudeLossless compressionComputer sciencePower (physics)Surface waveElectronic engineeringCommunications systemRange (aeronautics)Surface (topology)OpticsGrapheneOptoelectronicsElectrical engineeringTelecommunicationsPhysicsEngineeringMathematicsData compressionArtificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

Terahertz (THz) communication is considered to be a pivotal technology in new-generation communication networks. Due to its nature, THz waves attenuate significantly. A blockage will result in a significant power loss, therefore a line-of-sight (LOS) is required. Reconfigurable intelligent surface (RIS) technology is considered to be a promising solution to this problem. In literature, analyses are being done assuming that RIS having lossless reflections and continues phase shifts. In reality, surface reflections differ with the frequency of the incident wave and can have certain phase shifts. Moreover, the design tradeoff between the phase shift range and reflection amplitude should be considered. In this study, a graphene-based RIS has been designed and the communication performance is analyzed using its reflection coefficients and discrete phase shifts.

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: none
Teacher disagreement score0.576
Threshold uncertainty score0.268

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 head0.054
GPT teacher head0.252
Teacher spread0.198 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Communication TechnologiesFrench-language works237,207