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Record W2154612888 · doi:10.1109/lcomm.2008.080713

LP Decoding for Joint Source-Channel Codes and for the Non-Ergodic Polya Channel

2008· article· en· W2154612888 on OpenAlexaff
Adam Cohen, Fady Alajaji, Navin Kashyap, Glen Takahara

Bibliographic record

VenueIEEE Communications Letters · 2008
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsDecoding methodsErgodic theoryChannel (broadcasting)Binary symmetric channelChannel codeBinary numberAlgorithmComputer scienceList decodingMathematicsLow-density parity-check codeSequential decodingLinear programmingJoint (building)Coding (social sciences)Channel capacityTelecommunicationsConcatenated error correction codeBlock codeStatisticsArithmeticEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

Linear programming (LP) decoding of low-density parity-check codes over discrete memoryless symmetric channels was introduced by Feldman et al. in [1]. Here, we extend the LP decoding paradigm by applying it to two additional scenarios: joint source-channel (JSC) coding and decoding over the infinitememory non-ergodic binary Polya-contagion channel. Simulation results indicate that the JSC LP decoder yields significant gains over the standard LP decoder for non-uniform sources. Simulations also show that the LP decoder for the Polya channel performs moderately well in relation to the ϵ-capacity limit.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.098
GPT teacher head0.304
Teacher spread0.207 · 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.

Study designSimulation or modeling
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

Citations7
Published2008
Admission routes1
Has abstractyes

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