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Record W2133902375 · doi:10.1109/pccc.2004.1394944

Connection-level performance analysis for adaptive bandwidth allocation in multimedia wireless cellular networks

2005· article· en· W2133902375 on OpenAlex

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

VenueIEEE International Conference on Performance, Computing, and Communications, 2004 · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceHandoverCall blockingCall Admission ControlComputer networkQuality of serviceBlocking (statistics)Bandwidth (computing)Bandwidth allocationMarkov chainWireless networkChannel allocation schemesWirelessMarkov processDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose an adaptive bandwidth framework for supporting multiple classes of multimedia services with different quality of service (QoS) requirements in the next generation of wireless cellular networks. The framework combines the following components: (i) a threshold-based bandwidth allocation policy. (ii) an efficient threshold-type call admission control (CAC) algorithm, and (iii) a bandwidth adaptation algorithm (BAA). The framework can be modeled as a multi-dimensional Markov chain, and therefore, a product form solution is provided. The QoS metrics - new call blocking probability, handoff call dropping probability, and degradation probability are derived. The analytical results are supported by simulation and show that this work improves the service quality by minimizing the handoff call dropping probability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.001
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 head0.085
GPT teacher head0.326
Teacher spread0.240 · 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