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Record W2118847369 · doi:10.1109/ccece.2007.233

Fixed-Rate Raptor Code Performance Over Correlated Rayleigh Fading Channels

2007· article· en· W2118847369 on OpenAlexaff
B. Sivasubramanian, H. Leib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsFadingRaptor codeRayleigh fadingTurbo codeComputer scienceDecoding methodsAlgorithmSerial concatenated convolutional codesConcatenated error correction codeBlock code

Abstract

fetched live from OpenAlex

This work considers fixed-rate Raptor coded transmission over fading channels with memory following the Jakes' model. The effect of the normalized fade rate on the performance of low-to-moderate rate Raptor codes, when decoded with an improved soft-decision decoding algorithm, is investigated. Results show that, over slow fading channels, the performance of Raptor codes degrades from that of memoryless (fast) fading by 1.5-4.3 dB. Despite this degradation, Raptor codes are shown to provide substantial performance gains of 3-4 dB over serial and parallel concatenated turbo codes, demonstrating their effectiveness as high-performance codes over correlated fading channels.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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
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

Citations6
Published2007
Admission routes1
Has abstractyes

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