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Record W2110090728 · doi:10.1109/icassp.2008.4518306

Rate-optimal MIMO transmission with mean and covariance feedback at low SNR

2008· article· en· W2110090728 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCovarianceMIMOErgodic theoryCovariance matrixBeamformingComputer scienceMathematicsGaussianChannel (broadcasting)Eigenvalues and eigenvectorsAlgorithmMathematical optimizationControl theory (sociology)Applied mathematicsStatisticsTelecommunicationsArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

We consider a multiple-input multiple-output (MIMO) wireless communication scenario in which the channel follows a general spatially-correlated complex Gaussian distribution with non-zero mean. We derive an explicit characterization of the optimal input covariance from an ergodic rate perspective for systems that operate at low SNRs. This characterization is in terms of the eigen decomposition of a matrix that depends on the mean and the covariance of the channel, and typically results in a beamforming strategy along the principal eigenvector of that matrix. Simulation results show the potential impact of (jointly) exploiting the mean and the covariance of the channel on the ergodic achievable rate at both low and moderate- to-high SNRs.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.774

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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