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Record W2112401276 · doi:10.1109/ccece.2007.161

Efficient Medium Access Control for Broadband Powerline Communications Networks

2007· article· en· W2112401276 on OpenAlexaff
T. Chiras, Polychronis Koutsakis, M. Paterakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer networkComputer scienceLast mile (transportation)The InternetProtocol (science)BroadbandPower-line communicationInternet accessAccess controlMedia access controlAccess networkBroadband networksTransmission (telecommunications)TelecommunicationsMilePower (physics)WirelessOperating system

Abstract

fetched live from OpenAlex

Powerline Communications (PLC) are currently being considered as an alternative for high-speed data communications and Internet access. With multiple outlets in almost every room, power lines are already the most pervasive network in the home or small office. This work presents a new Medium Access Control (MAC) protocol for the "last mile" access PLC networks. Via an extensive simulation study, our protocol is compared to a well-known protocol from the literature in terms of the efficiency of the transmission of long messages; our protocol is shown to excel both in network utilization and in the average signaling delay required for the completion of the transmission request procedure, in both the cases of a lightly and heavily disturbed PLC network.

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)0.003
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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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