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Record W2150115251 · doi:10.1109/wimob.2008.72

Impact of Wireless Channel on VoIP QoS and Admission Regions in IEEE 802.11g WLANs

2008· article· en· W2150115251 on OpenAlex
Armelle Gnassou, Jean‐François Frigon, Brunilde Sansò

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer networkComputer scienceVoice over IPPhysical layerWireless networkQuality of serviceWirelessLink adaptationNetwork packetWireless Multimedia ExtensionsChannel (broadcasting)IEEE 802.11IEEE 802Network allocation vectorWi-Fi arrayFadingTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we evaluate the impact of the wireless channel and physical layer parameters on the performance of VoIP traffic in IEEE 802.11g networks. The wireless channel is modeled using a finite state Markov chain based on an adaptive modulation and coding scheme. The transition probabilities encompass the effects of the time-varying wireless channel, such as the Doppler frequency and the operating SNR, and the physical layer target packet error rate. This model is implemented inthe NS-2 tool and used to analyze the QoS (delay and packetdrop rate) of real-time multimedia traffic in realistic wireless channels. Numerical results showing the physical layer impact on the number of supported VoIP calls in an 802.11g network are presented. We also demonstrate that the admission region in the presence of mixed VoIP and data remains linear, even after considering the complex wireless channel effects.

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: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.478

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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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

Citations1
Published2008
Admission routes2
Has abstractyes

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