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Record W1922557894 · doi:10.1109/icc.2005.1494743

On the achievable spectral efficiency of adaptive transmission with transmit-beamforming

2005· article· en· W1922557894 on OpenAlexaff
Amine Maaref, Sonia Aı̈ssa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsSpectral efficiencyMIMORayleigh fadingBeamformingIndependent and identically distributed random variablesWishart distributionPrecodingComputer scienceTransmission (telecommunications)Probability density functionChannel capacityFadingTopology (electrical circuits)Control theory (sociology)MathematicsChannel (broadcasting)Mathematical optimizationTelecommunicationsRandom variableStatistics

Abstract

fetched live from OpenAlex

In this paper, we capitalize on some recently derived results yielding the probability density function (PDF) of the largest eigenvalue of complex central Wishart matrices with independent and identically distributed entries, to derive a closed-form expression for the capacity of adaptive transmission with the so-called multiple-input multiple-output (MIMO) maximal ratio combining systems, also known as transmit-beamforming (TB) systems, under Rayleigh fading. The achievable spectral efficiency by this type of MIMO systems is derived for two power and rate allocation policies, namely, the optimal power and rate adaptation policy (opra) and the channel inversion with fixed rate policy (cifr). The spectral efficiency of these adaptive transmission policies when used along with TB is evaluated and compared for different MIMO antenna configurations.

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 score0.937
Threshold uncertainty score0.270

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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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