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Record W2115474404 · doi:10.1109/wimob.2006.1696391

Dynamic Admission and Congestion Control for Real-time Traffic in IEEE 802.11e Wireless LANs

2006· article· en· W2115474404 on OpenAlex
Yi Liu, Smita Pawar, Chadi Assi, Aditi Agarwal

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
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsConcordia University
Fundersnot available
KeywordsQuality of serviceComputer scienceComputer networkAdmission controlThroughputChannel (broadcasting)IEEE 802Scheme (mathematics)Network congestionIEEE 802.11e-2005WirelessReal-time computingWireless networkWi-Fi arrayTelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

The emerging IEEE 802.11e standard for wireless local area networks (WLANs) has been proposed to support quality of service (QoS) by assigning different channel access parameters (CAPs) to different access categories (ACs). As an important part of QoS, an admission control scheme is required to maximally utilize the wireless medium resources and to efficiently admit the upcoming real time traffic while not compromising the QoS of existing traffic. In this paper, we propose a novel admission and congestion control scheme which obtains the admission control parameters through existing analytical model and traffic QoS requirements. It then dynamically updates the CAPs based on periodical monitoring of current channel conditions. Through numerical analysis and extensive simulation, results show that such a scheme could provide the guaranteed QoS for admitted real-time traffic in terms of guaranteed throughput achievement, bounded maximum delay and bounded maximum dropping rate while maintaining good channel utilization

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.435

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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