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Record W4313643684 · doi:10.1109/icjece.2022.3210237

On the Packet Decoding Delay of Linear Network Coded Wireless Broadcast Sur le délai de décodage des paquets de la diffusion sans fil codée par réseau linéaire

2023· article· fr· W4313643684 on OpenAlexvenueno aff
Mingchao Yu, Alex Sprintson, Parastoo Sadeghi

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2023
Typearticle
Languagefr
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsnot available
Fundersnot available
KeywordsNetwork packetDecoding methodsThroughputComputer scienceAlgorithmLinear network codingUnicastMathematicsComputer networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

We apply linear network coding (LNC) to broadcast a block of data packets from one sender to a set of receivers via lossy wireless channels, assuming that each receiver already possesses a subset of these packets (through previous systematic transmissions) and wants the rest. We aim to characterize the average packet decoding delay (APDD), which reflects how soon each data packet can be decoded by each receiver on average, and to minimize it without sacrificing throughput. To this end, we first derive closed-form lower bounds on the expected APDD of LNC techniques. We then prove that determining whether these lower bounds are tight is NP-hard and so is APDD minimization. We then prove that every throughput-optimal LNC technique can approximate the minimum expected APDD with a ratio between 4/3 and 2 and that this ratio is exactly 2 for random LNC (RLNC). We also show that instantly decodable network coding (IDNC) techniques cannot approximate APDD due to suboptimal throughput. Finally, we propose hypergraphic LNC (HLNC), a novel throughput-optimal and APDD-approximating technique based on a hypergraphic model of receivers. Our simulations show that the APDD of HLNC significantly outperforms existing techniques, including RLNC, under all considered settings without any sacrifice on throughput. Résumé—Nous appliquons le codage linéaire de réseau (LNC) pour diffuser un bloc de paquets de données d’un émetteur à un ensemble de récepteurs via des canaux sans fil avec pertes, en supposant que chaque récepteur possède déjà un sous-ensemble de ces paquets (par des transmissions systématiques précédentes) et veut le reste. Nous cherchons à caractériser le délai moyen de décodage des paquets (APDD), qui reflète la rapidité avec laquelle chaque paquet de données peut être décodé par chaque récepteur en moyenne, et à le minimiser sans sacrifier le débit. À cette fin, nous dérivons d’abord des limites inférieures en forme fermée sur l’APDD attendu des techniques LNC. Nous prouvons ensuite déterminer si ces limites inférieures sont serrées, NP-hard et que la minimisation de l’APDD l’est aussi. Nous prouvons ensuite que chaque technique LNC optimale en termes de débit peut approximer l’APDD minimum attendu avec un rapport entre 4/3 et 2 et que ce rapport est exactement 2 pour les LNC aléatoires (RLNC). Nous montrons également que les techniques de codage de réseau instantanément décodable (IDNC) ne peuvent pas approximer l’APDD en raison d’un débit sous-optimal. Enfin, nous proposons le LNC hypergraphique (HLNC), une nouvelle technique d’optimisation du débit et d’approximation de l’APDD basée sur un modèle hypergraphique des récepteurs. Nos simulations montrent que l’APDD de HLNC surpasse de manière significative les techniques existantes, y compris RLNC, dans tous les paramètres considérés sans aucun sacrifice sur le débit.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.231
Teacher spread0.205 · 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
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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

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