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Record W2130057124 · doi:10.1109/tsp.2007.896076

Multiuser Spectrum Optimization for Discrete Multitone Systems With Asynchronous Crosstalk

2007· article· en· W2130057124 on OpenAlexaff
Vincent M. K. Chan, Wei Yu

Bibliographic record

VenueIEEE Transactions on Signal Processing · 2007
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDigital subscriber lineAsynchronous communicationComputer scienceOrthogonal frequency-division multiplexingComputational complexity theoryOptimization problemKey (lock)Frequency domainInterference (communication)Mathematical optimizationAlgorithmTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Finding computationally efficient spectrum optimization methods for a multiuser digital subscriber line (DSL) environment is a key objective of dynamic spectrum management for DSL systems. For synchronous discrete multitone (DMT) based DSL systems, the computational complexity issue can be partially addressed using a dual optimization approach that decomposes the problem in the frequency domain. However, the decomposition approach fails whenever the DMT system is asynchronous, in which case the power allocations in adjacent frequency tones interfere with each other. This paper provides a mathematical model for intercarrier interference due to symbol misalignment and proposes ways to modify the dual method for spectrum optimization of asynchronous DSL systems. The main ingredient of the algorithm is an efficient method to evaluate the minimum power required to support a given bit-allocation using a power series approximation, combined with a local gradient search.

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)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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

Citations30
Published2007
Admission routes1
Has abstractyes

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