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Record W2133541616 · doi:10.1109/icc.2007.272

VoIP Capacity Allocation Using an Adaptive Voice Packetization Server in IEEE 802.11 WLANs

2007· article· en· W2133541616 on OpenAlexaff
Ahmad M. Kholaif, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVoice over IPComputer scienceComputer networkLatency (audio)IEEE 802Margin (machine learning)The InternetTelecommunicationsQuality of serviceOperating system

Abstract

fetched live from OpenAlex

WLAN VoIP capacity is known to be very low due to the effects of overheads at various protocol layers. An IEEE 802.11b access point (AP) operating at 11 Mbps for example, can support only about 12 G.711 voice connections with a 20 ms packetization interval. These effects can be mitigated by taking into account the available latency margin of the call and using it in the VoIP parameter selection. In this paper we propose the use of an adaptive voice packetization server (AVP-RTS) which splits the RTP VoIP connection into two legs. In this way each end of the call is negotiated separately and the server can allocate the available latency margin (and the ensuing capacity gain) asymmetrically across the call. We propose new algorithms for performing this capacity assignment and compare them to the conventional voice packetization scheme. Results from extensive simulations show that by using the AVP-RTS server we can significantly improve the multi-AP VoIP capacity for certain typical IEEE 802.11 situations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.638
Threshold uncertainty score0.603

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 communication0.0000.001
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 head0.074
GPT teacher head0.298
Teacher spread0.223 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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