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Record W2902814568 · doi:10.1109/wcsp.2018.8555566

Rate Matching and Piecewise Sequence Adaptation for Polar Codes with Reed-Solomon Kernels

2018· article· en· W2902814568 on OpenAlexaff
Ran Zhang, Hamid Saber, Yiqun Ge, Wuxian Shi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsPiecewiseKernel (algebra)Matching (statistics)AlgorithmSequence (biology)MathematicsBinary numberComputationComputer scienceDimension (graph theory)Discrete mathematicsArithmeticStatisticsCombinatorics

Abstract

fetched live from OpenAlex

The paper studies rate matching for polar codes with Reed-Solomon (RS) kernels. A low-complexity rate matching scheme, referred to as smallest index puncturing, is put forward with validity proof. To resolve the dramatically increased complexity of reliability sequence generation due to rate matching, a piecewise sequence adaptation method is designed. The method significantly cuts down the computation complexity while keeping a negligible performance loss. Simulation results demonstrate the performance gain of the 4-dimension RS kernel over the original binary 2-by-2 kernel under rate matching, and verify the efficacy of the proposed piecewise method.

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 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 score0.567
Threshold uncertainty score0.406

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.040
GPT teacher head0.291
Teacher spread0.251 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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