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Record W2149945936 · doi:10.1109/vetecs.2000.851369

Linear MMSE interference suppression in asynchronous random-CDMA

2002· article· en· W2149945936 on OpenAlex
Youngki Yoon

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
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCode division multiple accessMinimum mean square errorAsynchronous communicationComputer scienceBit error rateSpread spectrumWidebandProcess gainInterference (communication)Matched filterAlgorithmBandwidth (computing)MathematicsElectronic engineeringTelecommunicationsStatisticsDecoding methodsEngineeringDetectorEstimator

Abstract

fetched live from OpenAlex

This paper considers interference suppression, based on linear minimum mean square error (MMSE) filtering, in random code-division multiple-access (random-CDMA) systems such as IS-95 and wideband-CDMA using long pseudo-noise (PN) sequences. It examines the design of the MMSE receiver and its performance in terms of probability of bit error (P/sub e/). Expressions for P/sub e/ are derived for systems with multiple bit rates (and spreading factors), variable signal powers and chip pulse shaping. Numerical examples show substantial P/sub e/ gains in systems with one to two high-powered users and square-root raised cosine (Sqrt-RC) pulse shaping with 100% excess bandwidth.

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: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.044
GPT teacher head0.292
Teacher spread0.248 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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