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Record W2148397771 · doi:10.1109/icc.2009.5199171

Maximizing Throughput with Multiple Power Levels in a Random Access Infrastructure-Less Radio System

2009· article· en· W2148397771 on OpenAlexaff
Jahangir H. Sarker, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThroughputComputer networkNode (physics)Computer sciencePower controlRandom accessTransmission (telecommunications)IdleBase stationWireless networkWirelessMobile radioPower (physics)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

We propose and analyse a new random access protocol with multiple power levels selection schemes for infrastructureless wireless networks. In these networks, mobile nodes may communicate with each other without a central entity (base station), where each mobile node will be either in a transmitting mode or in a receiving mode or in an idle mode. Throughput with random power levels selection scheme is derived in terms of the transmission probability of each mobile node, receiving probability of each mobile node and the number of power levels. Throughput with optimum power levels selection scheme is also derived and compared with the previous one. Results show that the optimum transmission probability of each mobile node to achieve the maximum throughput depends only on the number of power levels. The maximum throughput region is devised in terms of transmission probability of each mobile node and the number of power levels. The proposed new random access protocol is truly distributive in nature and can be easily implemented in infrastructure-less wireless access systems without requiring any centralized control.

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)0.000
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 score0.928
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.021
GPT teacher head0.261
Teacher spread0.239 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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