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Record W1587378883 · doi:10.1109/glocom.2004.1378423

Blind channel estimation for orthogonal STBC in MISO systems

2005· article· en· W1587378883 on OpenAlexaff
Elzbieta Beres, Raviraj Adve

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCumulantChannel (broadcasting)AlgorithmMatrix (chemical analysis)FadingSpace–time block codeBlock codeComputer scienceEigendecomposition of a matrixMathematicsBlock (permutation group theory)Higher-order statisticsEstimation theoryMatrix decompositionAntenna (radio)SIGNAL (programming language)Eigenvalues and eigenvectorsTelecommunicationsSignal processingDecoding methodsStatisticsPhysicsCombinatorics

Abstract

fetched live from OpenAlex

The paper presents a closed-form blind channel estimation scheme for Alamouti's and other orthogonal space-time block codes. Unlike other blind algorithms, the scheme is able to estimate the channels, to within a phase constant, in multiple-input single-output systems, i.e., systems that employ only one receive antenna. The channel matrix is estimated from the eigenvalue decomposition of the fourth order cumulant matrix of the output signal. The performance of the scheme depends upon the accuracy of the estimated cumulants, and thus a scheme to improve the cumulant matrix estimate is suggested. Using these improved cumulants, the algorithm performs very well in slowly fading 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.281
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2005
Admission routes1
Has abstractyes

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