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Record W2101678281 · doi:10.1109/twc.2008.06001

Precoder Design for Space-Time Coded MIMO Systems with Imperfect Channel State Information

2008· article· en· W2101678281 on OpenAlexaff
Jane W. Huang, Edward Au, Vincent K. N. Lau

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMIMOPrecodingChannel state informationFadingTransmitterComputer scienceChannel (broadcasting)Control theory (sociology)Covariance matrixMathematicsAlgorithmConstraint (computer-aided design)Mathematical optimizationTelecommunicationsWireless

Abstract

fetched live from OpenAlex

We address the problem of designing a linear precoder for space-time coded multiple-input multiple-output (MIMO) system with imperfect knowledge of channel state information at the transmitter (CSIT), subject to a total transmit power constraint. Assuming an uncorrelated flat-fading channel and using a maximum likelihood decoder at the receiver, we analytically reveal that the derived precoder is a function of the noise variance, the eigenvalues of the estimated channel matrix and the eigenvalues of the codeword distance matrix. Furthermore, the power allocation on the eigenvalues of the precoder is shown to follow the water-pouring policy and hence the proposed precoder allocates more power to the stronger channels.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.027
GPT teacher head0.242
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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations13
Published2008
Admission routes1
Has abstractyes

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