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Record W1986263636 · doi:10.1007/s11235-010-9410-3

Adaptive pulse-shaped OFDM with application to in-home power line communications

2011· article· en· W1986263636 on OpenAlexafffund
Salvatore D’Alessandro, Andrea M. Tonello, Lutz Lampe

Bibliographic record

VenueTelecommunication Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGuard intervalComputer scienceOrthogonal frequency-division multiplexingOverhead (engineering)Guard (computer science)Power-line communicationNarrowbandWidebandElectronic engineeringChannel (broadcasting)TelecommunicationsPower (physics)Physics

Abstract

fetched live from OpenAlex

We propose an adaptive pulse-shaped orthogonal frequency division multiplexing (OFDM) scheme where the overhead and active sub-channels are adapted to jointly maximize capacity and fulfill the notching mask for coexistence with other devices operating in the same spectrum. The overhead includes the guard interval to partly compensate the channel dispersion, and the roll-off factor used by the shaping window. We show that significant gains are obtained compared to the case of using a fixed guard interval and roll-off factor. To simplify the implementation complexity we also propose to limit the amount of adaptation by deploying a finite set of overhead parameters. Numerical results are reported for typical wideband power line communication indoor channels.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.252
Teacher spread0.213 · 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

Citations27
Published2011
Admission routes2
Has abstractyes

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