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Record W2138896442 · doi:10.1109/icc.2007.617

Min-Max Congestion in Interference-Prone Wireless Mesh Networks

2007· article· en· W2138896442 on OpenAlexaff
Sonia Waharte, Ali Farzan, Raouf Boutaba

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkInterference (communication)Wireless mesh networkSoftware deploymentBandwidth (computing)Wireless networkDistributed computingWirelessTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Users' demand of seamless connectivity has pushed for the development of alternatives to traditional infrastructure networks. Potential solutions should be low-cost, easily deployable and adaptive to the environment. One approach that has gained tremendous attention over the past few years consists in deploying a backbone of access points wirelessly interconnected, offering users access to the wired infrastructure via multi-hop communication. However, the limited transfer capacities and the interference resulting from a shared transmission medium can prevent further deployment if the network performance does not meet users' expectations. In this work, we explore different ways to improve the nominal network capacity while accounting for the phenomena of intra-interference (interference on a single path) and inter-interference (interference among flows on different paths). In particular, under the assumptions of splitable traffic flows, we present an interference-aware linear- programming formulation of the min-max congestion problem with and without constraints on the path length. Finally, we evaluate via simulations the potential bandwidth capacity gain resulting from the implementation of these different approaches.

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: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.649

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 studies0.0000.000
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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
Published2007
Admission routes1
Has abstractyes

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