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Record W2167320586 · doi:10.1109/ccece.2004.1347720

A study of DiffServ based QoS issues in next generation mobile networks

2004· article· en· W2167320586 on OpenAlexaff
Thimma V. J. Ganesh Babu, Alagan Anpalagan, J.F. Hayes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia UniversityToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceQuality of serviceComputer networkIP Multimedia SubsystemInterconnectivityCode division multiple accessCellular networkNext-generation networkMobile QoSRadio resource managementAccess networkThe InternetService (business)TelecommunicationsService providerWireless networkWireless

Abstract

fetched live from OpenAlex

To provide data rates of the order of hundreds of Mbps and multimedia services, standardization efforts for next generation (4G) systems are focusing on target technologies and seamless connectivity through various types of networks, including wireline networks and WLANs. Different types of multiple access techniques, such as the ones based on multicarrier CDMA and OFDM (orthogonal frequency division multiplexing) have been proposed. There is a need for functional integration of the multiple networks, and, with the evolution of IPv6 and QoS support for IP networks, an IP based interconnectivity is best suited. A QoS aware adaptive radio resource management technique based on multi-code multicarrier CDMA is discussed. We develop a novel radio access method and develop algorithms for allocating and controlling radio network resources so that system performance can be maximized and guaranteed QoS for multimedia services can be provided within the DiffServ 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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.083
GPT teacher head0.338
Teacher spread0.255 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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