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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 OpenAlexaff
Weiwei Wang, Zihua Guo, Xuemin Shen, Changjia Chen, Jun Cai

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2008
Admission routes1
Has abstractyes

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