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Record W2132159417 · doi:10.1109/jsac.2005.864008

Outage probability analysis of a MC-DS-CDMA system with variable repetition code rate and spreading factor

2006· article· en· W2132159417 on OpenAlexaff
Yongfeng Chen, E.S. Sousa

Bibliographic record

VenueIEEE Journal on Selected Areas in Communications · 2006
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSubcarrierComputer scienceRepetition codeCode division multiple accessCode rateCode (set theory)Bit error rateChannel (broadcasting)AlgorithmOutage probabilityFadingTelecommunicationsElectronic engineeringStatisticsMathematicsBlock codeDecoding methodsLinear codeOrthogonal frequency-division multiplexing

Abstract

fetched live from OpenAlex

In the multicarrier direct-sequence code-division multiple access with repetition code system, decreasing the code rate can result in greater frequency diversity. However, to keep the same transmit bit rate, the spreading factor in each subcarrier needs to be reduced. In this paper, we use the outage probability as a criterion for these two system parameters' design. We first examine the condition on code rate for the uncorrelated fading channel assumption. The system performance is derived and the outage probability is obtained in closed forms for equal and unequal transmit bit energy scenarios. The relation between the spreading factor and the rate of the repetition code is investigated by observing the outage probability equation.

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)0.002
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.290
Teacher spread0.254 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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