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Record W2102369107 · doi:10.1109/tvt.2009.2031456

Observation-Based Time-Varying MIMO Channel Model

2009· article· en· W2102369107 on OpenAlexaff
T.J. Willink

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsMIMONarrowbandTransmitterRayleigh fadingFadingAutocorrelationChannel (broadcasting)Computer scienceProbability density functionCoupling (piping)Electronic engineeringAlgorithmMathematicsEngineeringTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

This paper presents a method to model and simulate time-varying narrowband multiple-input-multiple-out (MIMO) channels based on observations from measured data. The data were obtained in a vehicular urban environment, with a fixed transmitter array and a mobile receiver array. The measured channel response matrices were decomposed to isolate the coupling from the transmitter eigenbasis to the received eigenbasis, as in the Weichselberger model. These complex coupling elements have been characterized and seen to comprise directional components that may be Ricean or Rayleigh fading. The Rayleigh fading directional components can be well modeled using the von Mises probability density function, which is parameterized for the time-varying model using the measured data. The model has been validated by comparing the mutual information and eigenstructure autocorrelation characteristics of its output with those of the measured data. The statistical nature of the model means that different realizations can be generated, each representative of the originating data.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.213
Teacher spread0.202 · 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
GenreMethods

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

Citations8
Published2009
Admission routes1
Has abstractyes

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