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Record W2111061487 · doi:10.1109/26.996072

Noncoherent MMSE interference suppression for DS-CDMA

2002· article· en· W2111061487 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

VenueIEEE Transactions on Communications · 2002
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCode division multiple accessMinimum mean square errorAlgorithmInterference (communication)Spread spectrumSingle antenna interference cancellationMatched filterComputer scienceMathematicsDetectorControl theory (sociology)Channel (broadcasting)Electronic engineeringTelecommunicationsDecoding methodsStatisticsEngineering

Abstract

fetched live from OpenAlex

A novel robust noncoherent receiver for minimum mean-squared error (MMSE) interference suppression for direct-sequence code-division multiple access (DS-CDMA) is proposed. The receiver consists of a linear MMSE filter and a decision-feedback differential detector (DF-DD). The performance of the proposed scheme is investigated analytically and by computer simulations. It is shown that the loss compared to coherent MMSE interference suppression is limited and can be made arbitrarily small by increasing the observation window used for calculation of the reference symbol of the DF-DD. Hence, the regarded noncoherent receiver is near-far resistant. For adjustment of the MMSE filter coefficients three noncoherent adaptive algorithms are proposed. In contrast to coherent adaptive algorithms, these noncoherent algorithms have the important advantage that they also converge if the channel phase is time-variant.

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 categoriesOpen science
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 score0.934
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0060.000
Research integrity0.0000.001
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 head0.117
GPT teacher head0.335
Teacher spread0.218 · 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