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Record W1997006052 · doi:10.1049/ip-com:20040365

Performance analysis of multicarrier CDMA systems with parallel and serial concatenated coding in fading channels

2004· article· en· W1997006052 on OpenAlexaff
P.L. Kafle, A.B. Sesay

Bibliographic record

VenueIEE Proceedings - Communications · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceTurbo codeFadingBit error rateAlgorithmConvolutional codeCode division multiple accessConcatenated error correction codeRayleigh fadingMultipath propagationTurbo equalizerSerial concatenated convolutional codesElectronic engineeringTelecommunications linkDecoding methodsTelecommunicationsChannel (broadcasting)Block codeEngineering

Abstract

fetched live from OpenAlex

The authors present a bit error rate performance analysis of multicarrier code division multiple access (MC-CDMA) systems with turbo and serial concatenated convolutional coding (SCCC) in multipath fading channels. The performance analysis is done for maximal ratio combining and minimum mean square error combining detection in the downlink system. Upper bounds to the average bit error probability are presented for a punctured turbo code and for an SCCC code of similar decoding complexity. These analytical bounds are derived for fully interleaved Rayleigh fading channels. The bit error rate performance is also verified by simulations in the regions of low signal-to-noise ratios. The analytical and simulation results illustrate the relative merits of the turbo and SCCC codes for MC-CDMA systems and their suitability to achieve very low error rates in wireless data applications.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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
Published2004
Admission routes1
Has abstractyes

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