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Record W2089058644 · doi:10.1145/1454659.1454674

Efficient multimedia call delivery over IP-based heterogeneous wireless access networks

2008· article· en· W2089058644 on OpenAlexafffund
Peyman TalebiFard, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkWireless networkIP Multimedia SubsystemHeterogeneous networkNext-generation networkThe InternetWirelessQueueing theoryAccess networkDistributed computingTelecommunicationsQuality of serviceWorld Wide Web

Abstract

fetched live from OpenAlex

Next generation of heterogeneous wireless access networks are expected to interwork with IP-based infrastructures. The existing call delivery approaches in cellular networks may not be well suited for future communication systems because they suffer from unnecessary usage of network resources for call attempts that may fail which adds to excessive signaling delays and queuing costs. As a result, heavy load will be imposed on the network for the case of multimedia calls. The future users of communication systems will subscribe to both Internet Protocol (IP)-based and Circuit Switched (CS) based services and in the foreseeable future a single database that handles user profiles across all domains will be required. Hence, the number of queries and retrievals from the database will have a significant impact on the network performance. We present a new scheme based on Reverse Virtual Call setup (RVC) as a solution to the call delivery problem in heterogeneous wireless networks and demonstrate the performance of this framework in terms of signaling cost and delay.

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: none
Teacher disagreement score0.690
Threshold uncertainty score0.995

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 science0.0050.002
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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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
Published2008
Admission routes2
Has abstractyes

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