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Record W2144442340 · doi:10.1109/infocom.2006.153

Effective Packet Scheduling with Fairness Adaptation in Ultra Wideband Wireless Networks

2006· article· en· W2144442340 on OpenAlexaff
Hai Jiang, Weihua Zhuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Waterloo
FundersU.S. Department of Defense
KeywordsComputer scienceComputer networkScheduling (production processes)WirelessExploitQuality of serviceNetwork packetWireless networkFairness measureLink adaptationDistributed computingChannel (broadcasting)FadingTelecommunicationsThroughputEngineeringComputer security

Abstract

fetched live from OpenAlex

Ultra-wideband (UWB) transmission is an emerging wireless technology, and medium access control (MAC) with quality of service (QoS) provisioning is essential to coordinate the access among competing devices in UWB-based wireless networks. In this paper, we study the exclusive region concept (which was previously proposed) to determine the active set of senders at a time. We find out that, different from the previous work, the exclusive region for a specific link should be a system-level concept, and should depend on system factors such as interference from/to other active links. Based on the findings, two MAC packet scheduling schemes are proposed to exploit the system capacity and, at the same time, to achieve a certain level of fairness in UWB wireless networks. As the long acquisition time in UWB transmission can significantly reduce the system efficiency, the proposed schemes can be modified to alleviate the negative effect of a long acquisition time. Computer simulations demonstrate the effectiveness and efficiency of our proposed schemes

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2006
Admission routes1
Has abstractyes

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