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Hybrid Full-Wave/Ray-Tracing Propagation Modeling of Reconfigurable Intelligent Surface-Enabled Communication Channels

2022· article· en· W4296911179 on OpenAlexaff
Yuanzhi Liu, Costas D. Sarris

Bibliographic record

Venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRay tracing (physics)TransmitterComputer scienceChannel (broadcasting)Surface waveCommunications systemElectronic engineeringRadar cross-sectionWave propagationSurface (topology)Finite element methodRadio propagationAcousticsRadarOpticsEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper introduces a hybrid numerical modeling method that combines full-wave modeling with ray-tracing for reconfigurable intelligent surface (RIS)-enabled communication channels. Full-wave simulation is used to obtain the radiation pattern of the transmitter and the complex radar cross-section (RCS) of the RIS, while a ray-tracer is employed to model the interaction between the RIS and the environment. The proposed method is validated with a case study of a typical RIS-enabled communication channel that was modeled by the proposed hybrid method and by full-wave finite element analysis. Excellent agreement has been observed between the two methods.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.251
Teacher spread0.226 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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