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Record W2167091338 · doi:10.1155/2008/546184

An Analytical Model for Optimum Byte‐Level and Packet‐Level FEC Assignment Using Buffer Dynamics

2008· article· en· W2167091338 on OpenAlex

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

VenueJournal of Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsByteComputer scienceNetwork packetBuffer overflowForward error correctionComputer networkPacket lossChannel (broadcasting)Real-time computingAlgorithmComputer hardwareDecoding methods

Abstract

fetched live from OpenAlex

This letter presents an analytical model that jointly exploits the buffer dynamics of both the sending and receiving nodes to find the optimum number of byte‐level and packet‐level forward error correction (FEC) units for real‐time multimedia transmission over wireless networks. The proposed analytical model first provides an optimum number of FEC units required at the byte‐level, and then chooses the number of FEC units at the packet‐level based on current channel and network conditions. The accuracy of the proposed model is dependent on two parameters: the variable deadline‐time at the byte‐level and fixed round‐trip time (RTT) delay at the packet‐level. Numerical results demonstrate the effectiveness of the model in reducing the unrecoverable error probability, which is achieved when the byte‐level FEC scheme is supplemented by the packet‐level FEC 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.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.496
Threshold uncertainty score0.721

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 science0.0000.000
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 head0.034
GPT teacher head0.237
Teacher spread0.203 · 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