MétaCan
Menu
Back to cohort
Record W2111746960 · doi:10.1109/wcnc.2004.1311636

Fair resource allocation with statistical QoS support for multimedia traffic in a wideband CDMA cellular network

2004· article· en· W2111746960 on OpenAlexaff
Liang Xu, Xuemin Shen, J.W. Mark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceQuality of serviceComputer networkFadingTelecommunications linkCode division multiple accessResource allocationCellular networkScheduling (production processes)Channel (broadcasting)Radio resource managementReal-time computingTelecommunicationsWirelessWireless networkEngineering

Abstract

fetched live from OpenAlex

A dynamic fair resource allocation scheme to efficiently support realtime and nonrealtime multimedia traffic with guaranteed statistical QoS in uplinks of wideband CDMA cellular networks is proposed. The scheme uses the generalized processor sharing (GPS) fair service discipline to allocate uplink channel resources, taking into account the characteristics of channel fading and inter-cell interference. For realtime traffic, the weight of each user is assigned according to a required delay bound. For nonrealtime traffic, the weight of each traffic flow is adjusted dynamically according to fading channel states and a statistical fairness bound requirement. The capacity gain achieved by the proposed dynamic-weight scheme is analyzed for different fairness bound requirements. Simulation results are presented to demonstrate the performance of the proposed dynamic-weight scheduling scheme in terms of radio resource utilization and guaranteed statistical QoS, in comparison with the conventional static-weight scheme.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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
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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207