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Record W2130517475 · doi:10.1109/spawc.2001.923862

Semi-blind spatio-temporal equalization and multiuser detection for DS-CDMA systems

2002· article· en· W2130517475 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlind equalizationCode division multiple accessCyclostationary processMultiuser detectionSubspace topologySpread spectrumEstimatorComputer scienceEqualization (audio)AlgorithmConstant (computer programming)Asynchronous communicationInterference (communication)Synchronous CDMAChannel (broadcasting)Speech recognitionMathematicsArtificial intelligenceTelecommunicationsStatisticsDecoding methods

Abstract

fetched live from OpenAlex

We propose a novel strategy for semi-blind spatio-temporal equalization and multiuser detection for short-burst asynchronous DS-CDMA systems. The technique is based on first performing semi-blind subspace-based channel identification and then semi-blind equalization via the constant modulus algorithm (CMA). In this way we are able to improve upon the traditional training-based least squares (LS) estimator by adding the constant modulus and cyclostationary properties of communication signals. Simulation results indicate a significant reduction in the required number of training symbols (for a short burst of data) compared with regularized LS estimators (both training-based and semi-blind). Application is in the reverse link of third-generation DS-CDMA systems.

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: Methods · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.379

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 communication0.0000.001
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 head0.047
GPT teacher head0.278
Teacher spread0.230 · 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

Quick stats

Citations8
Published2002
Admission routes1
Has abstractyes

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