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Record W1680800573 · doi:10.1109/icct.2000.889181

Turbo coding for transmission over ADSL

2002· article· en· W1680800573 on OpenAlexaff
Liang Zhang, Abbas Yongaçoğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTrellis modulationAsymmetric digital subscriber lineDigital subscriber lineComputer scienceTurbo codeCoding gainAdditive white Gaussian noiseCoding (social sciences)Computer networkTurboConvolutional codeBit error rateElectronic engineeringAlgorithmDecoding methodsFadingEngineeringMathematicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

A multidimensional 16-state trellis coded modulation (MTCM) is given as an option in the asymmetric digital subscriber line (ADSL) standard based on discrete multitone (DMT) techniques. To further improve the transmission performance, instead of this MTCM, we propose to use bandwidth-efficient turbo trellis coded modulation (TTCM) in ADSL DMT systems. We show that by using turbo codes, we can obtain 6 dB coding gain for a bit error rate (BER) of 10/sup -6/ in AWGN channels and more than 6.8 dB coding gain for a BER of 10/sup -7/ using a concatenated coding scheme.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations5
Published2002
Admission routes1
Has abstractyes

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