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Record W2804385454 · doi:10.1109/isplc.2018.8360201

Feasibility of full-duplex dynamic spectrum management for PLC-DSL coexistence

2018· article· en· W2804385454 on OpenAlexaff
Gautham Prasad, Lutz Lampe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
FundersInstituto de Telecomunicações
KeywordsDigital subscriber linePower-line communicationComputer scienceElectronic engineeringInterference (communication)BroadbandDuplex (building)Spectrum managementTransmitter power outputElectromagnetic compatibilityChannel (broadcasting)Broadband networksComputer networkTelecommunicationsEngineeringWirelessCognitive radioPower (physics)TransmitterPhysics

Abstract

fetched live from OpenAlex

In this paper, we address the issue of electromagnetic compatibility (EMC) in indoor wired communication systems. In particular, we consider the electromagnetic interference between broadband power line communications (BB-PLC) and digital subscriber line (DSL) networks, and investigate a non-intrusive opportunistic dynamic spectrum management technique to enable coexistence. To this end, we examine the feasibility of power spectral density adaptation at the PLC nodes using spectrum sensing to estimate the DSL-to-BB-PLC interference channel. We consider the use of in band full duplexing to enable power line modems with superior spectrum sensing efficiency to simultaneously transmit PLC data and sense for the electromagnetically coupled DSL signal. We then determine the conditions under which sufficient signal-to-noise ratio of the known DSL pilot signal is achieved at the PLC node to obtain a satisfactory interference channel estimate. Further, we simulate a realistic indoor BB-PLC network and use real DSL-to-PLC interference channel measurement data to examine the viability of a dynamic spectral adaptation approach.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.286

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 head0.030
GPT teacher head0.294
Teacher spread0.265 · 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
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

Citations1
Published2018
Admission routes1
Has abstractyes

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