MétaCan
Menu
Back to cohort
Record W2543901804 · doi:10.1109/camsap.2007.4498005

Linear Precoders for OSTBC Mimo Systems with Correlated Rayleigh Fading Channels Based on Convex Optimization

2007· article· en· W2543901804 on OpenAlexaff
Khoa T. Phan, Sergiy A. Vorobyov, Chintha Tellambura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMOPrecodingChannel state informationRayleigh fadingFadingTransmitterConvex optimizationInterference (communication)Computer scienceControl theory (sociology)MathematicsSpatial correlationWirelessAlgorithmChannel (broadcasting)Mathematical optimizationRegular polygonTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we present a precoder design framework and a computationally simple precoding technique for OSTBC based MIMO wireless systems with both transmit and receive correlations for the case of Rayleigh fading. It is assumed that the correlation among receive antennas is independent of the correlation among transmit antennas (and vice versa). The transmit and receive correlation matrices are assumed to be available at the transmitter, while the instantaneous channel state information (CSI) is unknown. The proposed precoder minimizes the upper bound on the symbol error rate (SER). Our main contribution consists of developing a convex formulation for originally non-convex problem of SER minimization for precoder design. Additionally, it can be shown that previously known solutions for some special cases of precoder design naturally follow from our more general results. Numerical simulations illustrate the improved performance of the proposed precoders in terms of the output SER.

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: Methods · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.958

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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
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

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced MIMO Systems OptimizationFrench-language works237,207