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Record W2171506231 · doi:10.1109/newcas.2008.4606368

High-speed design of adaptive LDPC codes for wireless networks

2008· article· en· W2171506231 on OpenAlexafffund
Zhiyong He, Sébastien Roy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversité Laval
FundersCMC Microsystems
KeywordsLow-density parity-check codeComputer scienceEncoderRaptor codeTurbo codeForward error correctionField-programmable gate arrayThroughputWirelessParallel computingDecoding methodsComputer hardwareAlgorithmTelecommunicationsError floor

Abstract

fetched live from OpenAlex

In this paper, we propose a class of adaptive low-density parity-check (LDPC) codes for reliable data transmission in wireless networks, i.e. the code rate can be adapted according to channel conditions to maximize the total capacity. Constructed from shifted identity matrices, the first advantage of the proposed codes is that these codes are particularly well-suitable for the high-speed implementation of parallel encoders and parallel decoders. Since a unique mother parity check matrix is used to construct LDPC codes with several code rates, the second great advantage of the proposed codes is that a single universal encoder (decoder) is adequate to encode (decode) multi-rate codes, which makes it possible to efficiently implement multi-rate LDPC codes in a subscriber station. The implementation results into field programmable gate array (FPGA) devices indicate that a universal parallel encoder for LDPC codes with 9 code rates is capable of reaching a throughput above 3.6 Gigabit per second by using a clock frequency of 300 MHz and consuming only 1% of the total resources in a typical FPGA device.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.489

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 head0.052
GPT teacher head0.261
Teacher spread0.209 · 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 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

Citations3
Published2008
Admission routes2
Has abstractyes

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