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Record W2110040147 · doi:10.1109/tcomm.2010.07.080633

Limits to Performance of Optimum Combining with Dense Multiple Correlated Antennas

2010· article· en· W2110040147 on OpenAlexaff
Payam Dehghani Rahimzadeh, Norman C. Beaulieu

Bibliographic record

VenueIEEE Transactions on Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMathematicsRayleigh fadingSignal-to-interference-plus-noise ratioInterference (communication)Signal-to-noise ratio (imaging)FadingTopology (electrical circuits)Mathematical analysisPower (physics)StatisticsPhysicsTelecommunicationsComputer scienceCombinatoricsChannel (broadcasting)

Abstract

fetched live from OpenAlex

An analytical expression is derived for the average output signal-to-interference-plus-noise ratio of optimum combining for a spatially correlated array of antennas in the presence of a single interferer and Rayleigh fading. Using the derived expression and based on an asymptotic analysis of the eigenvalues of dense correlation matrices, the asymptotic performance of optimum combining is evaluated as the number of the antennas increases while the total physical size of the array is fixed. Two different scenarios are considered, namely, fixed average received power per antenna and fixed total average received power. It is shown that in the former scenario, the average output signal-to-interference-plus-noise ratio is asymptotically a linear function of the number of the antennas while in the latter scenario it eventually saturates at a certain value. The slope of the asymptotic line in the former scenario as well as the value of the saturation limit in the latter scenario are derived in terms of the point spectrum of the underlying array correlation function. The case of multiple interferers is examined by simulation and is shown to exhibit similar asymptotic behaviour to the case of one interferer.

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 score0.675
Threshold uncertainty score0.630

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 head0.017
GPT teacher head0.232
Teacher spread0.216 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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