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Record W2269333319 · doi:10.1049/iet-com.2015.0361

Multi‐way relaying for cooperative indoor power line communications

2015· article· en· W2269333319 on OpenAlexafffund
Moslem Noori, Lutz Lampe

Bibliographic record

VenueIET Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer sciencePower-line communicationRelayDiversity gainWirelessBroadbandMultiplexingTransmission (telecommunications)Computer networkCommunications systemAttenuationPower (physics)TelecommunicationsElectronic engineeringChannel (broadcasting)Real-time computingFadingEngineering

Abstract

fetched live from OpenAlex

Sustaining high data rates over an indoor broadband power line communication (PLC) link is a challenging task due to the significant attenuation of high frequency signals transmitted over power cables. Repeaters are often used to alleviate the problem of signal attenuation, however, this comes at a loss of multiplexing gain. On the other hand, modern relaying concepts, as used in wireless systems, are not as effective in PLC setups due to the absence of diversity improvement. In this study, the authors address this issue and propose a new approach to improve the multiplexing gain of relay‐aided PLC systems. The authors consider a multi‐user indoor PLC scenario where all users want to share their data. For this setup, the authors then advocate the application of amplify‐and‐forward (AF) multi‐way relaying (MWR). Considering the practical constraints of an indoor PLC system, the authors study the achievable data rates of AF MWR, and compare them to those achievable with direct (non‐cooperative) transmission and conventional relaying schemes. The simulations demonstrate that depending on the network topology, MWR can result in significant performance improvement. The authors further discuss how their rate analysis can be exploited to decrease the energy consumption in the system.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.124
GPT teacher head0.341
Teacher spread0.217 · 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.

Study designNot applicable
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

Citations7
Published2015
Admission routes2
Has abstractyes

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