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Ray-Tracing Driven ANN Propagation Models for Indoor Environments at 28 GHz

2020· article· en· W3132181967 on OpenAlexaff
Aristeidis Seretis, Takahiro Hashimoto, Kun Zeng, Costas D. Sarris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRay tracing (physics)TracingComputer scienceRadio propagationSignal strengthArtificial neural networkSIGNAL (programming language)Feed forwardFeedforward neural networkBackpropagationArtificial intelligenceSimulationAcousticsReal-time computingTelecommunicationsOpticsEngineeringPhysicsControl engineeringWireless

Abstract

fetched live from OpenAlex

Ray-tracing is widely used for radio propagation modeling of indoor environments, such as hallways and offices. Shooting and bouncing ray-tracing methods are faster than fullwave methods in such environments, especially as the frequency of operation increases following the new 5G specifications. Still, a machine learning approach can generalize a few ray-traced points into full signal strength maps of arbitrary resolution. In this paper, a feedforward standard artificial neural network is trained by ray-tracing data at 28 GHz to predict signal strength in a Γ -shaped corridor. The network's accuracy in reconstructing the actual signal levels is on par with that of the ray-tracer.

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 score0.840
Threshold uncertainty score0.532

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 head0.036
GPT teacher head0.214
Teacher spread0.178 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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