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Record W2127446774 · doi:10.1109/ccece.2003.1226217

Bandwidth-efficient transmission using nonbinary CRSC turbo codes

2004· article· en· W2127446774 on OpenAlexaff
Yizhao Gao, Mohammad Soleymani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceTurbo codeConvolutional codePhase-shift keyingEncoderSerial concatenated convolutional codesTurbo equalizerElectronic engineeringAdditive white Gaussian noiseBit error rateBandwidth (computing)Forward error correctionTurboDecoding methodsAlgorithmConcatenated error correction codeChannel (broadcasting)TelecommunicationsBlock codeEngineering

Abstract

fetched live from OpenAlex

Since power and bandwidth are limited resources in modern digital communications systems, the subject of this paper is the design of nonbinary circular recursive systematic convolutional (CRSC) turbo codes combined with high level modulation, such as 8PSK modulation. It includes how to choose and/or design the components in a turbo encoder, as well as the design of the interleaver and the puncturer, to get the best possible performance with simplified Max-Log-MAP algorithm and higher transmission rate. Several combination schemes of coding and modulation, and their complexity are discussed. A comprehensive study over AWGN channel is carried out to show the performance of the proposed coding schemes and their potential as an alternative for more effective and efficient transmission of data via satellites without increasing the required bandwidth.

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)0.003
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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