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Record W2167220150 · doi:10.1109/newcas.2014.6934013

An encoder/decoder with throughput over Gigabits/sec for rate-compatible LDPC codes with wide code rates

2014· article· en· W2167220150 on OpenAlexaff
Zhiyong He, Paul Fortier, Sébastien Roy, X. Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversité de SherbrookeUniversité Laval
FundersChinese Academy of Sciences
KeywordsLow-density parity-check codeComputer scienceEncoderCode rateParallel computingThroughputLinear codeDecoding methodsAlgorithmBlock codeTelecommunicationsWireless

Abstract

fetched live from OpenAlex

A challenge in the design of rate-compatible (RC) low-density parity-check (LDPC) codes is how to maximize the range of code rates. In this paper, we propose a class of RC LDPC codes with a very wide range of code rates. To ensure linear encoding, dual-diagonal form for the parity part of the mother parity-check matrix is used. Constructed from shifted identity matrices, the proposed codes are particularly well-suited for the high-speed implementation of parallel encoders and parallel decoders. To widen the range of code rates, we have proposed an optimal transmission scheme, which keeps the optimal degree distribution unchanged for the mother code and all daughter codes. Thus, the proposed technique pushes the upper bound of code rates to 0.96, which is the highest rate in RC LDPC codes in the world, based on our best knowledge. The implementation results into field programmable gate array (FPGA) devices indicate that a parallel encoder (decoder) for the proposed RC LDPC codes is capable of reaching a throughput of 7.2 (1.8) Gigabits per second using a clock frequency of 150 MHz.

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 categoriesMeta-epidemiology (narrow)
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 score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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