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Record W2099114934 · doi:10.1109/tcomm.2007.895978

Convergence Speed and Throughput of Analog Decoders

2007· article· en· W2099114934 on OpenAlex
Saied Hemati, Amir H. Banihashemi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Communications · 2007
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsConvergence (economics)ThroughputComputer scienceElectronic engineeringEngineeringTelecommunicationsWireless

Abstract

fetched live from OpenAlex

This letter is concerned with the implementation of iterative decoding algorithms in analog integrated circuits. We study the convergence speed and the throughput of analog decoders for low-density parity-check codes, and show that they depend on the code, the decoding algorithm, the signal-to-noise ratio, and the average time constant of the analog circuit interconnections. However, they are not a function of the variance of the time constants. The analysis presented here can be used for selecting suitable codes and decoding algorithms for analog decoding. Furthermore, it can be used to estimate the throughput of an analog decoder, if the average time constant of the analog circuit is known

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.

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 score0.843
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.318
Teacher spread0.276 · 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