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Record W2509323143 · doi:10.1109/lwc.2016.2601609

Analytical Approximation of Packet Delay Jitter in Simple Queues

2016· article· en· W2509323143 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 Wireless Communications Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsJitterComputer scienceNetwork packetQueueing theoryQuality of serviceComputer networkProcessing delayScheduling (production processes)Real-time computingQueueTransmission delayPacket analyzerMathematical optimizationMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Delay jitter of data packets is known to be a crucial quality of service measure especially for real-time applications (e.g., VoIP). It takes place as a result of the queuing, scheduling, and routing latencies within the network. However, control schemes that directly tackle the jitter problem in today's advanced wireless systems are rare. To enable such schemes, proper modeling of the packet delay jitter is an essential preliminary step. This letter presents a comprehensive mathematical modeling for the packet delay jitter in a simple queuing system with one traffic buffer of infinite length, one server, and single hop. In contrast to independent and identically distributed models, our analysis focuses on the correlated nature of service intervals. The presented models study different scenarios and parameters for the queue in terms of the system's utilization and the probability distribution of data packets' service and interarrival times, respectively. Numerical simulations demonstrate the high accuracy achieved by the presented models.

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 score0.368
Threshold uncertainty score0.548

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 science0.0010.000
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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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