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Record W2147764054 · doi:10.1109/vetec.1998.686445

Modeling and resource allocation in wireless multimedia CDMA systems

2002· article· en· W2147764054 on OpenAlexaff
M. Soleimanipour, Weihua Zhuang, G.H. Freeman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceQuality of serviceComputer networkResource allocationWireless networkWirelessCode division multiple accessMulti-frequency networkMultimediaBase stationContext (archaeology)Wireless Multimedia ExtensionsPower controlRadio resource managementHandoverWi-Fi arrayTelecommunicationsPower (physics)

Abstract

fetched live from OpenAlex

To comply with future communications needs, it is desirable to provide multimedia services at wireless access points. In a wireless multimedia context, resource allocation is crucial because the channel quality varies with time and the network resources are scarce. We address the resource allocation problem in wireless multimedia CDMA (code division multiple access) systems. The objectives are: (1) to develop a wireless multimedia model based on CDMA to provide data rate and quality of service (QoS) on demand and to integrate various traffic types efficiently, and (2) to develop a mathematical programming problem to optimize resource allocation based on a wireless service provider's perspective. In comparison with the previous work, the proposed multimedia model supports a wider range of applications and service qualities, and combines power control, rate allocation and base station assignment with controlled handoff switching and dropped calls in a multi-cell environment.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.268
Teacher spread0.220 · 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
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

Citations18
Published2002
Admission routes1
Has abstractyes

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