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Record W2407693711 · doi:10.1109/isplc.2016.7476258

Enhancing transmission efficiency of broadband PLC systems with In-Band Full Duplexing

2016· article· en· W2407693711 on OpenAlexaff
Gautham Prasad, Lutz Lampe, Sudip Shekhar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceBroadbandPower-line communicationEthernetSpectral efficiencyThroughputWirelessInterference (communication)Transmission (telecommunications)Digital subscriber lineElectronic engineeringChannel (broadcasting)Computer networkPower (physics)Noise (video)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, we investigate the feasibility of In-Band Full-Duplexing (IBFD) for Broadband Power Line Communication (BB-PLC) systems, to potentially double the throughput and spectral efficiency. IBFD accomplishes this through simultaneous bidirectional communication over the same powerline in the same frequency band, by applying echo cancellation (EC) to suppress the interference caused by the self-transmitted signal. In light of various EC schemes employed in Digital Subscriber Lines, Ethernet, co-axial cables and recently in wireless systems, we investigate the specific requirements and constraints in BB-PLC, and present solutions for an effective IBFD implementation for such systems. We then use our simulated EC gain values to examine the overall data rate gains obtained by IBFD under different channel attenuations and PLC noise conditions, to determine if we truly obtain a 100% increase in throughput at all conditions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.180

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 head0.008
GPT teacher head0.205
Teacher spread0.198 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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