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Record W2024606306 · doi:10.1109/tvt.2007.901871

A Proof Toward Optimality of a Combined Rate, Power, and Cell Control Algorithm Employed in a Cellular CDMA Network

2007· article· en· W2024606306 on OpenAlexaff
K. Hazaveh, Alagan Anpalagan

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2007
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPower controlCode division multiple accessBase stationCellular networkAlgorithmPower (physics)Interference (communication)Flexibility (engineering)Transmitter power outputOptimal controlSpread spectrumComputer scienceAlgorithm designMathematical optimizationMathematicsComputer network

Abstract

fetched live from OpenAlex

Hanly and Yates proposed algorithms for combined power and cell (PIC) control that proved to be an optimal solution to P/C control if such a solution exists. Anpalagan and Sousa have proposed a combined rate, power, and cell (R/P/C) control algorithm with the main idea of interference balancing between base stations. They proposed an algorithm which was empirically proved to yield the optimal solution in terms of the average transmit bit energy of the system. In this paper, a mathematical proof is given that shows the optimality of the R/P/C algorithm. The R/P/C algorithm gives flexibility in setting the rate in response to the congestion level in the network, and hence, it has applications in the areas of delay-tolerant data services.

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)0.004
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.004
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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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
Published2007
Admission routes1
Has abstractyes

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