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Record W2095828264 · doi:10.1109/vetecf.2007.212

A Novel Chase Based Multiuser Detector for MIMO-CDMA Systems

2007· article· en· W2095828264 on OpenAlexaff
Feng Liu, M. Reza Soleymani

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2007
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsDetectorMIMOChaseComputer scienceScheme (mathematics)Multiuser detectionCode division multiple accessElectronic engineeringAlgorithmTelecommunicationsMathematicsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper proposes a new chase based multiuser detection scheme for MIMO-CDMA systems. The proposed approach provides performance gain by concentrating on improving the detection accuracy of the weakest symbol. Compared to the layered space-time multiuser detector (LAST-MUD), the proposed scheme can achieve substantial performance improvement, especially, when the number of transmit antennas is equal to the number of receive antennas. The comparison between our scheme and another chase based scheme, the B-Chase detector, which was originally proposed for improving the performance of the vertical Bell Laboratories layered space-time (V-BLAST) system, is also presented. The problem existing in the B-Chase detector is the criterion for selecting the weakest symbol. In our scheme, a more reasonable selection criterion is proposed. We show that the proposed scheme has less complexity than the B-Chase detector but with the tendency of achieving better performance at high SNR.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.048
GPT teacher head0.303
Teacher spread0.256 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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