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Record W2138855354 · doi:10.1109/ccece.2004.1344996

Effect of oversimplifying the simulated indoor propagation on the deterministic MIMO capacity

2004· article· en· W2138855354 on OpenAlexaff
M.S. Elnaggar, Safieddin Safavi‐Naeini, S.K. Chaudhuri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMIMOChannel capacityComputer scienceElectronic engineeringRadio propagationChannel (broadcasting)Ray tracing (physics)Reflection (computer programming)Multipath propagationMatrix (chemical analysis)Rayleigh fadingSpatial correlationRadio frequencyFadingAlgorithmTopology (electrical circuits)TelecommunicationsPhysicsEngineeringOpticsElectrical engineeringMaterials science

Abstract

fetched live from OpenAlex

MIMO systems have shown promising information capacity gain in a multipath-rich environment. Evaluation of the MIMO achievable capacity requires knowledge of the RF channel matrix. A common approach is to assume a Rayleigh fading channel and, accordingly, evaluate the statistical average of the capacity. This simple approach neglects the dependence of the capacity on the specific propagation environment. Alternatively, in this work, we use a deterministic approach based on the simulation of the RF propagation channel matrix for specific scenarios, which includes the propagation characteristics. Consequently, the RF channel matrix can be found, yielding the deterministic MIMO capacity. The objective of this work is to show how an oversimplification of the RF simulation characteristics (number of rays, planar/non-planar reflection tracing and wall thickness) affects the evaluated deterministic MIMO capacity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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
Published2004
Admission routes1
Has abstractyes

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