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Record W2099440376 · doi:10.1109/bsc.2010.5473022

On code design in joint MAC scheduling and wireless network coding

2010· article· en· W2099440376 on OpenAlexaff
Raheleh Niati, Amir H. Banihashemi, Thomas Kunz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceWireless networkWirelessLinear network codingComputer networkWi-Fi arrayCoding (social sciences)Wireless WANCounterexampleRadio resource managementScheduling (production processes)Distributed computingTheoretical computer scienceTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

In this paper, we investigate the problem of network coding and media scheduling in wireless multihop networks. Unique characteristics of the wireless media, such as omnidirectional transmissions and destructive interference, as well as the limitations of wireless nodes to either transmit or receive at any given time, imply new code design constraints for wireless networks. In this work, following the approach of Sagduyu et al., given a sufficient set of conflict-free link sets, we investigate necessary and sufficient conditions to design a capacity-achieving network coding solution for wireless networks. This approach uses a wired graph representation of the wireless network to design wireless network codes. Our study shows that the proposed approach works only if we preserve all of the code characteristics of the wired graph in the wireless one. We show the shortcoming of previous studies through a counterexample and explore this case further. The resulting insight is summarized in Theorem 1, which proves that unequal time shares allocated to conflict-free link sets will result in changing coding coefficients as the sets with larger time share are scheduled repeatedly.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.058
GPT teacher head0.283
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 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

Citations5
Published2010
Admission routes1
Has abstractyes

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