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Record W2125161617 · doi:10.1109/icsmc.2004.1400909

Scheduling bursty data at WCDMA downlink using fuzzy inference

2005· article· en· W2125161617 on OpenAlex
Jun Xu, Kevin Li

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
KeywordsComputer scienceTelecommunications linkScheduling (production processes)Code division multiple accessMaximum throughput schedulingFuzzy logicComputer networkDistributed computingWirelessWireless networkDynamic priority schedulingMathematical optimizationRound-robin schedulingQuality of serviceArtificial intelligence

Abstract

fetched live from OpenAlex

One of the major concerns for wideband code division multiple access (WCDMA) wireless network dimensioning is to improve the downlink capacity to accommodate more data traffic. Due to the "soft capacity" of CDMA system, there exists a conflict between the maximum throughput and the fairness for data users. To deal with the tradeoff, we propose a scheduling scheme using fuzzy inference involving factors of dynamics of traffic and wireless channels, and preferences of the decision maker. An exemplar design is given to improve the data throughput. Simulation results show that the fuzzy scheduler has superior performance to regular rate allocation schemes, and it increases the capacity of WCDMA that is elastic in nature. Moreover, the, fuzzy approach demonstrates its robustness, flexibility, and simplicity in the decision-making process when human preference is considered.

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: Methods · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score0.627

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 communication0.0000.001
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 head0.047
GPT teacher head0.290
Teacher spread0.243 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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