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Record W2141997901 · doi:10.1109/wcnc.2007.138

Rate-Compatible Punctured Systematic Repeat-Accumulate Codes

2007· article· en· W2141997901 on OpenAlexaff
Shiva Kumar Planjery, T. Aaron Gulliver, Andrew Thangaraj

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPuncturingLow-density parity-check codeTurbo codeConcatenated error correction codeAdditive white Gaussian noiseSerial concatenated convolutional codesAlgorithmLinear codeComputer scienceBlock codeRaptor codeForward error correctionMathematicsTheoretical computer scienceDecoding methodsError floorChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

In this paper, we present rate-compatible systematic repeat-accumulate (RA) codes for the additive white Gaussian noise (AWGN) channel. The systematic form is used because of the higher degree parity checks we require in the code. We show that very high code rates can be attained with good performance through our puncturing schemes. Although RA codes are very simple in terms of complexity compared to other codes such as turbo codes or low density parity check (LDPC) codes, the performance of these codes is quite competitive. Codes with rates up to 9/10 are obtained from a single rate 1/3 systematic regular RA code. Performance results show that our puncturing provides superior performance at high code rates compared to just puncturing parity bits.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2007
Admission routes1
Has abstractyes

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