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Record W2160947970 · doi:10.1109/cwit.2009.5069538

Cross-layer Raptor coding for broadcasting over wireless channels with memory

2009· article· en· W2160947970 on OpenAlex
Yu Cao, Steven D. Blostein

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsErasureComputer scienceRaptor codeOnline codesFountain codeDecoding methodsBinary erasure channelComputer networkErasure codeForward error correctionTornado codeNetwork packetLuby transform codeWirelessRelayChannel (broadcasting)Concatenated error correction codeAlgorithmTelecommunicationsChannel capacityBlock code

Abstract

fetched live from OpenAlex

Raptor codes are a class of rateless codes that have been shown to provide promising performance in erasure channels, and more recently, in noisy channels. This paper investigates the performance of application layer Raptor codes for broadcasting services over wireless channels with memory. A hybrid erasure-soft decoding algorithm is proposed as a cross-layer protocol for application layer raptor codes. These protocols relay corrupted packets into the application layer. The resulting hybrid error-erasure channels are modeled by a hierarchical Markov channel model. Capacity evaluation and simulation results show that the proposed cross-layer decoding algorithms outperform existing erasure decoding schemes significantly without any modification to the transmitter. The effects of channel memory and other parameters are also studied by simulation.

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: Empirical · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score0.671

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 communication0.0000.001
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 head0.032
GPT teacher head0.310
Teacher spread0.278 · 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

Quick stats

Citations9
Published2009
Admission routes2
Has abstractyes

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