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Record W2135635739 · doi:10.1109/vetecf.2003.1286356

A simple model for forward link power-based blocking in CDMA systems

2003· article· en· W2135635739 on OpenAlexaff
Mazin Al‐Shalash, J.A. Khoja, J.W. Bredow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsComputer scienceCode division multiple accessBlocking (statistics)Erlang (programming language)CDMA spectral efficiencyQueueing theorySimple (philosophy)WirelessWirelineComputer networkNear-far problemCall blockingDistributed computingTelecommunicationsTheoretical computer science

Abstract

fetched live from OpenAlex

The use of simple models based on queuing theory for the prediction of call blocking, has a long history in telephony. Formulas such as Erlang B, Erlang C, and others have been applied successfully to wireless as well as wireline systems. A simple model for the soft blocking criteria employed in CDMA systems, particularly those based on forward link power, is however lacking. Such a model must take into account the statistics of the forward link power, as well as the characteristics of the traffic. The need for such a model is exacerbated by the adoption of CDMA as the access technology for various 3rd generation wireless systems. This paper presents a simple model for forward link power-based blocking in CDMA systems. The accuracy of the model is indicated by comparing its predictions against the results obtained from a fully dynamic CDMA system simulator.

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.054
GPT teacher head0.319
Teacher spread0.264 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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