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Record W2146816449 · doi:10.1109/ccece.2003.1226261

A channel condition dependent QoS enabling scheme for IEEE802.11 Wireless LAN and its Linux based implementation

2004· article· en· W2146816449 on OpenAlexaff
D. Liu, Dimitrios Makrakis, Voicu Groza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer networkComputer scienceDynamic bandwidth allocationTelecommunications linkQuality of serviceService setWirelessChannel allocation schemesBandwidth allocationBandwidth (computing)Scheduling (production processes)Channel (broadcasting)Wireless networkWi-Fi arrayTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper presents a distributed adaptive bandwidth allocation scheme, designed to provide quality of service support in IEEE 802.11 wireless LANs. A centralized bandwidth manager that runs at the access point performs the dynamic bandwidth allocation based on the realtime wireless channel condition and the service policy of downlink and uplink traffic. A set of distributed bandwidth agents, which are located on the mobile hosts, perform uplink traffic scheduling according to the quality of service requirement of different applications that are running at the devices. At the same time, real-time wireless channel monitors provide information regarding the condition of the wireless channel to the centralized bandwidth manager. In this paper, we discuss the design of the distributed adaptive bandwidth allocation scheme and the interactive signaling mechanism between the centralized manager and the distributed agents. Furthermore, we set up and provide results from our experimental IEEE 802.11b wireless LAN test environment, where we have implemented the designed technology. A thorough test and performance analysis are also presented in this paper.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.491

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 science0.0000.000
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 head0.031
GPT teacher head0.316
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations2
Published2004
Admission routes1
Has abstractyes

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