MétaCan
Menu
Back to cohort
Record W1894472550 · doi:10.1109/vetec.1993.508813

DFE convergence for interference cancellation in spread spectrum multiple access systems

2002· article· en· W1894472550 on OpenAlexafffund
M. Abdulrahman, A.U.H. Sheikh, D.D. Falconer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpread spectrumCode division multiple accessComputer scienceSingle antenna interference cancellationInterference (communication)Multipath propagationMultipath interferenceBit error rateElectronic engineeringConvergence (economics)Code (set theory)Communications systemDecoding methodsMultiuser detectionAlgorithmTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

A fractionally spaced decision feedback equalizer (DFE) is proposed for code division multiple access (CDMA) in an interference-dominated environment. This system assumes no knowledge of the interferers' spreading codes. The calculation of optimum receiver taps shows good improvement in the capacity of the proposed system over others. The DFE receiver is simulated in a multiuser multipath environment. Performance results show the expected convergence of the DFE, with and without knowledge of the desired user's spreading code. Results also show the effect of sudden birth and death of interferers in the system. The bit error rate of the simulated DFE system is presented.

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)0.006
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.111
GPT teacher head0.325
Teacher spread0.214 · 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
Published2002
Admission routes2
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207