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Record W2032600114 · doi:10.1145/1143549.1143747

Performance evaluation for unsolicited grant service flows in 802.16 networks

2006· article· en· W2032600114 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRetransmissionComputer scienceQuality of serviceComputer networkThroughputService (business)Automatic repeat requestLossy compressionLimit (mathematics)Task (project management)Simple (philosophy)Channel (broadcasting)WirelessTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, the performance of unsolicited grant service (UGS) connections defined in IEEE 802.16 Std. is investigated. A simple admission control strategy based on the periodic exhaustive service principle is first introduced to provide Quality of Service (QoS) satisfaction for such connections. The task of system parameter selection and performance evaluation for the UGS flows is tackled. In particular, the maximum retransmission limit that works along with the embedded Automatic Repeat reQuest (ARQ) mechanism is emphasized. A novel transferred model is formulated such that the lossy characteristic of the wireless channels in real networks can be completely engineered by way of the traffic arrival pattern and the size of the messages in the proposed model. Significant merits and efficiency have been identified in the proposed model through extensive simulation, where a complete match between the analytical and simulation results is observed.

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: none
Teacher disagreement score0.618
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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