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Record W2127820012 · doi:10.1109/icc.2005.1494705

A subspace-based iterative group blind multiuser detection and decoding for coded CDMA systems

2005· article· en· W2127820012 on OpenAlexaff
S. Talakoub, Behnam Shahrrava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCode division multiple accessMultiuser detectionDecoding methodsSingle antenna interference cancellationComputer scienceTurboTelecommunications linkInterference (communication)Subspace topologyMinimum mean square errorAlgorithmBit error rateChannel (broadcasting)Turbo codeElectronic engineeringTelecommunicationsMathematicsArtificial intelligenceEngineeringStatistics

Abstract

fetched live from OpenAlex

A turbo multiuser receiver is proposed for the uplink of channel-coded code-division multiple-access (CDMA) systems with unknown interference. The proposed receiver consists of a first stage that performs soft interference cancellation and group blind linear minimum mean-square error (MMSE) filtering followed by a second stage of channel decoding. The proposed group blind filter satisfies the MMSE criterion to suppress the remained interference caused by known users based on the spreading sequences and the channel characteristics of these users and blindly suppress the remained interference caused other unknown users using a subspace-based method. The proposed receiver is suitable for suppressing high intercell interference. By exchanging soft information between the first and second stages, the receiver performance is improved through iteration. Simulation results show that the proposed group blind receiver significantly outperforms the conventional turbo multiuser detector in the presence of intercell interference.

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)0.001
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 score0.904
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.048
GPT teacher head0.312
Teacher spread0.264 · 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
Published2005
Admission routes1
Has abstractyes

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