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Record W2143397028 · doi:10.1109/pimrc.1995.480976

Congestion control in signalling free hybrid ATM/CDMA satellite network

2002· article· en· W2143397028 on OpenAlexaff
A.K. Elhakeem, Michel Kadoch, Ning Zhou, M. S. R. Murthy M. S. R. Murthy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsNortel (Canada)École de Technologie SupérieureConcordia University
Fundersnot available
KeywordsTime division multiple accessComputer networkAsynchronous Transfer ModeComputer scienceTelecommunications linkNetwork congestionCode division multiple accessCall controlCellular networkTime-division multiplexingSubframeAsynchronous communicationTelecommunicationsMultiplexing

Abstract

fetched live from OpenAlex

We pursue a performance analysis for computing the various performance criteria in a hybrid time division/asynchronous transfer mode/code division multiple access network, i.e. TDMA/ATM/CDMA network. Users accessing this TDMA/ATM/CDMA uplink frame are assumed to belong to one of 4 service classes, namely video, voice, file and interactive data. Each user accesses only a portion of the subframe slots assigned to its class. A variable frame boundary strategy is used to adjust the subframe boundaries depending on the call load. To alleviate congestion in the assumed hubless signalling free satellite network, the satellite measures the uplink traffic of each class and issues pilot congestion control indicators to on-going calls of each class. These will be subsequently used by ground users to control their activities and police their calls using modified versions of leaky bucket and virtual leaky bucket congestion control techniques. The new techniques alleviate many of difficulties of specific slot assignment, onboard call management, superframe counting and management involved in the state of the art TDMA based systems, and yield a call establishment free yet dynamic and very well controlled access technique.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
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 head0.031
GPT teacher head0.240
Teacher spread0.209 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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