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Record W2052749401 · doi:10.1109/bsc.2008.4563244

An FPGA implementation of a soft-in soft-out decoder for block codes

2008· article· en· W2052749401 on OpenAlexaff
Abdul-rafeeq Abdul-Shakoor, R. Kerr, J. Lodge, V. Szwarc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceHamming codeVHDLSoft-decision decoderSoft errorComputer hardwareParallel computingClock rateBlock (permutation group theory)Decoding methodsEmbedded systemBlock codeAlgorithmElectronic engineeringEngineeringMathematicsChip

Abstract

fetched live from OpenAlex

This paper presents an FPGA implementation of the vector SISO algorithm for the (64, 57) extended Hamming code (EH) and (64, 51) extended Bose, Chaudhri, and Hocquenghem code (EBCH). The decoder architecture is defined in VHDL and the circuit is implemented on a Xilinx XC2VP100-1704ff-5 FPGA device. To achieve the required throughput, a pipelined data path architecture operating off a master clock was selected. To reduce gate count, the dynamic range of intermediate results was limited through use of saturation arithmetic. The decoder functionality was verified by means of a test bench that compared the decoded bit stream with error free transmitted signals. SISO decoder design choices that impact the bit error rate (BER) are also presented.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.402

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 science0.0010.000
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 head0.039
GPT teacher head0.352
Teacher spread0.312 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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