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Record W2084772994 · doi:10.1109/glocom.2011.6133937

A Clustering Approach to Autonomous Spectrum Balancing Using Multiple Reference Lines for DSL

2011· article· en· W2084772994 on OpenAlexaff
S. Huberman, Christopher Leung, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsMcGill University
Fundersnot available
KeywordsDigital subscriber lineCrosstalkComputer scienceCluster analysisProcess gainSpectrum managementDistributed computingSpread spectrumElectronic engineeringReal-time computingComputer networkCognitive radioWirelessTelecommunicationsEngineeringCode division multiple accessArtificial intelligence

Abstract

fetched live from OpenAlex

Crosstalk is the limiting factor of performance in Digital Subscriber Line systems. The use of Dynamic Spectrum Management (DSM) can reduce the effects of crosstalk. For DSM algorithms, there is an important trade-off between the complexity of an algorithm and its performance. This paper introduces a method of applying cluster theory to Autonomous Spectrum Balancing using Multiple Reference Lines in order to gain a favorable trade-off more consistently. It is shown that the achievable performance is very close to that of existing state-of-the-art DSM algorithms, while requiring significantly fewer time-consuming interference measurements.

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: Methods · Consensus signal: none
Teacher disagreement score0.562
Threshold uncertainty score0.477

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 head0.097
GPT teacher head0.261
Teacher spread0.164 · 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
GenreMethods

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

Citations4
Published2011
Admission routes1
Has abstractyes

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