MétaCan
Menu
Back to cohort
Record W1571416809 · doi:10.1109/wescan.1988.27663

Performance of CSMA with priority acknowledgements on intrabuilding power line local area networks

2003· article· en· W1571416809 on OpenAlexaff
J.O. Onunga, R.W. Donaldson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPollingComputer scienceComputer networkChannel (broadcasting)Frequency-shift keyingNetwork packetLine (geometry)ThroughputPower (physics)Power controlPower-line communicationCarrier sense multiple access with collision avoidanceSecurity tokenRangingReal-time computingTelecommunicationsWirelessMathematics

Abstract

fetched live from OpenAlex

The use and performance of carrier-sense multiaccess (CSMA) with priority acknowledgements (PA) for medium access control on intrabuilding power-line communication networks are described. Such networks, while universal in building coverage at no incremental installation cost and readily accessed, suffer from highly variable unpredictable signal-to-noise ratio. The architecture of the CSMA implementation is discussed, along with its advantages relative to token-passing or polling-access control schemes. Choice of the best packet length, in terms of throughput maximization, approximates 1000 bits. Delay-throughout performance is measured, and the effects of performance of the power-line channel behavior are ascertained. Extensive tests were conducted using channel bit rates from 1200 to 9600 b/s, with FSK (frequency-shift keyed) and spread-spectrum PSK (phase-shift keyed) modems transmitting at 3 V rms at frequencies ranging from 40 to 120 kHz. Included are results for potentially difficult cases involving transmissions across power phases.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicPower Line Communications and NoiseFrench-language works237,207