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Record W2130609588 · doi:10.1109/twc.2008.070277

Dynamic Bandwidth Allocation for QoS Provisioning in IEEE 802.16 Networks with ARQ-SA

2008· article· en· W2130609588 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
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of ManitobaUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkQuality of serviceBandwidth (computing)Automatic repeat requestBandwidth allocationAcknowledgementProvisioningProtocol data unitDynamic bandwidth allocationSelective Repeat ARQReal-time computingHybrid automatic repeat requestNetwork packetTelecommunications link

Abstract

fetched live from OpenAlex

Bandwidth allocation, in terms of distributing available data slots among different users, is studied for QoS provisioning in IEEE 802.16 networks. By considering the Automatic Repeat reQuest with Selective Acknowledgement (ARQ-SA) scheme for erroneous wireless channels, a mathematical model is established to theoretically analyze the delay performance of transmitting Service Data Unit (SDU) under a multiuser environment. The analytical results indicate that the delivery delay of the SDU is dominated by the time spent for the first transmission of all its Protocol Data Units (PDUs). Based on this observation, a novel dynamic bandwidth allocation algorithm is proposed and a detailed performance analysis is provided. Simulation results show that the proposed bandwidth allocation algorithm can significantly improve the delay performance of SDUs and ensure the fairness among different users.

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 score0.928
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.238
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