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Record W2162835436 · doi:10.1109/ciss.2006.286421

Rate Control and Dynamic Dimensioning of Multihop Wireless Networks

2006· article· en· W2162835436 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer networkComputer scienceWireless networkWireless WANWirelessDimensioningMunicipal wireless networkWi-Fi arrayHeterogeneous networkDistributed computingWireless distribution systemKey distribution in wireless sensor networksTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper is concerned with the problem of allocation of data transfer rates to the elastic applications in a multihop wireless network. While this is a well understood problem in wired networks, wireless networks present substantially different dimension to it. For one the very concept of link capacity, though obvious for wired links, is not so for wireless links in a network. Moreover, unlike a wired network, the traffic capacity of a wireless network can be varied simply by modifying certain protocol parameters; thus it is possible to dimension the network dynamically. The wireless networks we consider are so-called infrastructure-based networks implying that they are deployed by service providers by creating hot-spots through access points whereas the end applications running on user devices act as the end users. This permits the network and the users to be seen as separate economic entities competing and collaborating in response to each other. The aim of this paper is to precisely understand these issues and address the appropriate formulations based on them.

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: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.423

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 head0.001
GPT teacher head0.166
Teacher spread0.165 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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