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Record W4248646537 · doi:10.32920/ryerson.14649975

An Analysis of Pilot Power Based Power Control and Dynamic Load Sharing in Cellular CDMA Networks

2021· preprint· en· W4248646537 on OpenAlexfundno aff
Maheswaran Subramaniam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBase stationPower controlTransmitter power outputCellular networkComputer scienceMobile stationPower (physics)Code division multiple accessInterference (communication)Mobile telephonyComputer networkReal-time computingElectronic engineeringEngineeringMobile radioTransmitter

Abstract

fetched live from OpenAlex

Power control is one of the most important processes in cellular CDMA networks as the interference is the predominant factor that influences the capacity and signal to noise and interference ratio (SINR). In mobile communication, minimizing the mobile transmitted power subject to maintaining the link quality is a challenging task. In this thesis, a pilot based power control (PPBPC) algorithm integrated with base station assignment is proposed which is decentralized, uses transmit power control and adapts cell sizes for load distribution. In the proposed algorithm, each base station transmits its forward link pilot power inversely proportional to the total reverse link received power. The mobile station senses the strongest pilot power received and determines its home base station. Using the proposed algorithm, dynamic propogation of base station assignment occurs which leads to re-assignment of home base stations system-wide reducing the total mobile transmit power. The simulation results are the evidence for the feasibility of the implementation of the algorithm.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0040.003
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 head0.017
GPT teacher head0.287
Teacher spread0.269 · 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.

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
Published2021
Admission routes1
Has abstractyes

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