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Record W2611894512 · doi:10.1109/access.2017.2699967

Power Allocation in an RF Energy Harvesting DF Relay Network in the Presence of an Interferer

2017· article· en· W2611894512 on OpenAlexafffund
Lina Elmorshedy, Cyril Leung

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRelayComputer scienceThroughputComputer networkNode (physics)Channel state informationWirelessTransmission (telecommunications)Channel (broadcasting)Relay channelPower (physics)Energy harvestingWireless networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, we study a cooperative wireless relay network in which multiple sourcedestination pairs communicate via a decode-and-forward relay, which harvests energy from the source transmissions in the presence of an interfering signal. The goal is to efficiently distribute the relay's power among the different relay-destination (R - D) links. The outage probability and the throughput in the delaysensitive transmission mode are derived for the non-shared and several shared power allocation schemes. Numerical results show that the studied shared allocation schemes outperform the non-shared allocation scheme in terms of outage probability and throughput. Different shared allocation schemes are compared against each other in terms of outage probability, throughput, and fairness. The R - D channel dependent and the weighted-sum-rate maximization schemes achieve the best outage and throughput performances but require knowledge of the statistical channel state information at the relay node. The results also illustrate the tradeoff between the throughput and fairness of the different shared allocation schemes.

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 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: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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