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Record W2089426645 · doi:10.1109/tcomm.2006.873065

Selective maximum-likelihood symbol-by-symbol detection for multidimensional multicode WCDMA with precoding

2006· article· en· W2089426645 on OpenAlexaff
Dong In Kim

Bibliographic record

VenueIEEE Transactions on Communications · 2006
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPrecodingComputer scienceTelecommunications linkAlgorithmCode division multiple accessSymbol rateSymbol (formal)Electronic engineeringBit error rateDecoding methodsMIMOTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

To faciliate high-rate uplink transmission, a novel selective maximum-likelihood (SML) detection is realized on a set of parallel multicode (MC) channels as a result of MC wideband code-division multiple-access (WCDMA) signaling with precoding. Multidimensional (MD) signaling can be combined with MC to further increase the data rate with fixed spreading factor per symbol. In connection with this MDMC signaling, the proposed SML detection achieves significantly improved symbol-error probability, compared with other detection methods, both without soft sequential decoders for a tractable analysis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.840
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.021
GPT teacher head0.272
Teacher spread0.251 · 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.

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
Published2006
Admission routes1
Has abstractyes

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