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Record W2156078113 · doi:10.1109/iscas.2008.4542111

A 600-Mb/s encoder and decoder for low-density parity-check convolutional codes

2008· article· en· W2156078113 on OpenAlexaff
T.L. Brandon, J.C. Koob, Leendert van den Berg, Zhengang Chen, Amirhossein Alimohammad, Ramkrishna Swamy, Jason Klaus, Stephen Bates, Vincent Gaudet, B.F. Cockburn, D.G. Elliott

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEncoderSoft-decision decoderComputer scienceConvolutional codeThroughputLow-density parity-check codeDecoding methodsCode rateComputer hardwareParity bitAlgorithmReal-time computingElectronic engineeringEngineeringTelecommunicationsWireless

Abstract

fetched live from OpenAlex

A 600-Mb/s rate-1/2 (128,3,6) LDPC convolutional code encoder and decoder was implemented in a 90-nm CMOS process. The encoder operates at 1.1 GHz and includes built-in all-phase termination. The decoder design maximizes throughput while minimizing the number of memory banks and delivering an information throughput of 1 bit per clock cycle. The size of the decoder controller is minimized by sharing it among an arbitrary number of decoder processors. The decoder dissipates 0.61 nJ of energy per decoded information bit at an SNR of 2.0 and a throughput of 600 Mb/s. An integrated test system enables accurate power measurements for various SNR settings.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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 designTheoretical or conceptual
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
Published2008
Admission routes1
Has abstractyes

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