MétaCan
Menu
Back to cohort
Record W1976451251 · doi:10.1109/twc.2008.060530

Service Time Approximation in IEEE 802.11 Single-Hop Ad Hoc Networks

2008· article· en· W1976451251 on OpenAlex

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

VenueIEEE Transactions on Wireless Communications · 2008
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceWireless ad hoc networkQueueing theoryNetwork packetPoisson distributionComputer networkIEEE 802.11Bounded functionUpper and lower boundsService setProbability distributionQueue management systemWireless networkWirelessMathematicsStatisticsTelecommunicationsWi-Fi

Abstract

fetched live from OpenAlex

This paper investigates the near-memoryless behavior of the service time for IEEE 802.11 saturated single-hop ad hoc networks. We show that the number of packets successfully transmitted by any node over a time interval follows a general distribution, which is close to a Poisson distribution with an upper bounded distribution distance. The bound on the distribution distance is almost constant and is mainly affected by some system parameters and very slightly by the number of active nodes in the network. We also show that the service time distribution can be approximated by a geometric distribution. We illustrate that the usage of discrete-time queuing analysis (M/Geo/1) near network saturation greatly simplifies the queuing analysis and leads to sufficiently accurate results for both the first order statistics and the probability distribution of the number of packets in the queuing system. Computer simulation results demonstrate that the M/Geo/1 queuing model is very accurate.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.261
Teacher spread0.217 · 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