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Record W2144683195 · doi:10.1109/icc.2006.255665

Performance Enhancement for WLAN Supporting Integrated Voice/Data Traffic

2006· article· en· W2144683195 on OpenAlexafffund
Ping Wang, Hai Jiang, Weihua Zhuang

Bibliographic record

Venue2006 IEEE International Conference on Communications · 2006
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer networkComputer scienceVoice over IPQuality of servicePollingOverhead (engineering)ExploitWirelessWireless Multimedia ExtensionsThe InternetWireless networkTelecommunicationsWi-Fi arrayComputer security

Abstract

fetched live from OpenAlex

Voice over wireless local area network (VoWLAN) is an emerging application taking advantage of the promising voice over Internet Protocol (VoIP) technology and the wide deployment of WLANs all over the world. To fully exploit the capacity of WLAN supporting voice traffic, it is essential to explore statistical multiplexing and to suppress the large overhead. In this paper, we propose mechanisms to enhance the WLAN with voice quality of service (QoS) provisioning capability in supporting hybrid voice/data traffic. Voice multiplexing is achieved by a polling mechanism in the contention-free period and a deterministic priority access for voice traffic in the contention period. Header overhead for voice traffic is also reduced significantly. Delaytolerant data traffic is guaranteed an average portion of service time in the long run. A session admission control algorithm is presented to admit voice traffic into the system with QoS guarantee. Analytical and simulation results demonstrate the effectiveness and efficiency of our proposed solutions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0060.000
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 head0.146
GPT teacher head0.379
Teacher spread0.233 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2006
Admission routes2
Has abstractyes

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