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Record W2154176545 · doi:10.1109/glocom.2005.1577909

Error rate analysis of asynchronous multicode DS-CDMA systems

2005· article· en· W2154176545 on OpenAlex
Seung Joon Lee, Norman C. Beaulieu

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

VenueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005. · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCode division multiple accessComputer scienceAlgorithmSpread spectrumCode (set theory)Additive white Gaussian noiseGaussianBit error rateAsynchronous communicationSelection (genetic algorithm)Channel (broadcasting)Decoding methodsSet (abstract data type)TelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

A precise bit-error probability analysis method is derived for a multicode DS-CDMA system in an additive white Gaussian noise channel. The method is applicable to a multicode DS-CDMA system with an arbitrary number of multiple code sequences and any selection of multiple code sequences. The precise method gives results that discriminate the effect of the selection of multiple code sequences on the bit-error probability, whereas Gaussian approximations do not. Thus, the new method can be used to select the best multicode set for a given system, a task that cannot be achieved using Gaussian approximations. A two-step analytical procedure enables deriving an explicit, compact form for the characteristic function of the receiver decision statistic in a DS-CDMA system with an arbitrary number of multiple code sequences, and for any selection of multiple code sequences.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
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 score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0110.002
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 head0.047
GPT teacher head0.326
Teacher spread0.279 · 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