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Record W1778110641 · doi:10.1109/spawc.2005.1506291

Multirop transmission in power line communication networks. analysis and distributed space-time coding

2005· article· en· W1778110641 on OpenAlexaff
Lutz Lampe, Robert Schober, Shuwang Nu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRetransmissionComputer scienceComputer networkRepeater (horology)Node (physics)Linear network codingTransmission (telecommunications)Line codeTransmitterWireless ad hoc networkPower-line communicationChannel (broadcasting)WirelessPower (physics)TelecommunicationsBasebandEngineeringNetwork packetBandwidth (computing)Encoding (memory)

Abstract

fetched live from OpenAlex

In this paper, we consider transmission in relatively wide-stretched power line communication (PLC) networks, where repeaters are required to bridge the source-to-destination distance. In particular, it is assumed that each network node is a potential repeater and that multihop transmission is accomplished in an ad hoc fashion without the need for complex routing protocols. In such a scenario, due to the broadcasting nature of the power line channel, multiple repeater nodes may receive and retransmit the source message simultaneously. It is shown that, if no further signal processing is applied at the transmitter, simultaneous retransmission often deteriorates performance compared to single-node retransmission. We therefore advocate the application of distributed space-time block codes to the problem at hand.

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)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.223
Teacher spread0.217 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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