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Record W2084279706 · doi:10.1109/tcomm.2014.2347279

DSL and PLC Co-Existence: An Interference Cancellation Approach

2014· article· en· W2084279706 on OpenAlexaff
Khaled Ali, Geoffrey G. Messier, Stephen W. Lai

Bibliographic record

VenueIEEE Transactions on Communications · 2014
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDigital subscriber lineInterference (communication)Single antenna interference cancellationElectronic engineeringComputer sciencePower-line communicationChannel (broadcasting)Common-mode signalComputer networkEngineeringTelecommunicationsPower (physics)Digital signal processingPhysics

Abstract

fetched live from OpenAlex

It is often advocated that a solution to the problem of interference to digital subscriber line networks (DSL) from a power line communications (PLC) network is to prevent the PLC networks from utilizing the DSL spectrum. However, this solution will render PLC networks inoperable with the introduction of wide-band DSL technology like G.Fast. We propose utilizing an interference cancelling scheme to ensure the co-existence of DSL and PLC networks within the home environment. A measurement campaign is conducted where a set of 480 measurements are collected within a residential house to characterize the PLC-to-DSL interference environment for two DSL modem installation scenarios. The interference cancelling scheme is based on an adaptive frequency domain interference canceller (FDIC) that utilizes the common mode (CM) PLC interference to estimate the differential mode (DM) PLC interference. The effectiveness of the FDIC, which is insensitive to the non-stationarity of the PLC channel, is demonstrated using analysis that incorporates the measured PLC-to-DSL coupling 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.279
Teacher spread0.239 · 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 designBench or experimental
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

Citations21
Published2014
Admission routes1
Has abstractyes

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