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Record W2544808553

Modelling of electromagnetic propagation characteristics in indoor wireless communication systems using the LOD-FDTD method

2013· article· en· W2544808553 on OpenAlexaff
Menglin Zhai, Wen‐Yan Yin, Zhizhang Chen

Bibliographic record

VenueInternational Symposium on Antennas and Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFinite-difference time-domain methodWirelessLossy compressionPath lossComputer scienceElectronic engineeringChannel (broadcasting)Time domainComputational electromagneticsElectromagnetic fieldWave propagationFinite difference methodTelecommunicationsMathematicsEngineeringPhysicsMathematical analysisOpticsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Wireless technologies has been attracting much attention due to its wide application. Thus it is important to predict the electromagnetic propagation characterization precisely. Full-wave time-domain electromagnetic methods are usually effective in rigorously modelling and evaluating wireless channels. However, their computational expenditures are expensive, when dealing with electrically large size problems consisting of fine structures. Thus, in this paper, a rigorous full-wave numerical solution, via the locally one-direction finite difference time domain (LOD-FDTD) method for lossy media, which reduces computational time by removing the Courant-Friedrich-Levy (CFL) stability condition, is applied to characterize the wireless channel. By comparing the simulation results with the conventional FDTD, the proposed method demonstrates both good simulation efficiency and high accuracy. Post-processing of the simulation results lead to effective channel characterization with path loss exponent and probability distribution of path loss.

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: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.471

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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