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Ray-Tracing Simulations of mm-Wave Channels with FDTD-based Diffuse Scattering Models

2020· article· en· W3131641899 on OpenAlexaff
Stefanos Bakirtzis, Takahiro Hashimoto, Costas D. Sarris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Toronto
FundersAlexander S. Onassis Public Benefit Foundation
KeywordsFinite-difference time-domain methodRay tracing (physics)ScatteringComputer scienceChannel (broadcasting)WirelessDistributed ray tracingElectronic engineeringOpticsAlgorithmPhysicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Ray-tracing (RT) provides an accurate and cost-effective way to perform propagation modeling, assisting in the planning and optimization of wireless communication systems. To exploit its full potential in the challenging task of mm-wave channel modeling, it is important to include diffuse scattering. We use the Finite-Difference Time-Domain (FDTD) method to characterize rough surface scattering and derive diffuse scattering models that can be integrated with ray-tracing. This paper introduces this approach and presents simulation results for 5G indoor propagation scenarios.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.655
Threshold uncertainty score0.559

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 head0.055
GPT teacher head0.218
Teacher spread0.163 · 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
Published2020
Admission routes1
Has abstractyes

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