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Record W2100760407 · doi:10.1109/lawp.2007.909964

Analysis of a MIMO Outdoor Channel With Hybrid EM-Based Modeling

2007· article· en· W2100760407 on OpenAlexaff
Shirook Ali, F. Kohandani, Wen Geyi

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsBlackberry (Canada)
Fundersnot available
KeywordsMicrocellMIMOElectronic engineeringChannel (broadcasting)Multipath propagationAntenna (radio)Computer scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

We consider the effects of the local scatterers, the number of antennas, the mobile position, and the signal-to-noise ratio on the performance of a multi-antenna system via electromagnetic-based modeling. The urban microcell scenario is adopted and simulated using a hybrid electromagnetic model to obtain the channel matrix. Comparisons with the measurement-based spatial channel model (SCM) at frequency 2 GHz are made. We show that the channel condition improves as the multipath richness enhances. This also improves the capacity, which becomes more pronounced as the number of antennas increases. Our discussions show that the SCM does not reflect the actual system performance in some 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 score0.520
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

Explore more

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