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Record W1987532191 · doi:10.1109/tcomm.2015.2404777

Energy Harvesting Wireless Communications With Energy Cooperation Between Transmitter and Receiver

2015· article· en· W1987532191 on OpenAlexaff
Weiheng Ni, Xiaodai Dong

Bibliographic record

VenueIEEE Transactions on Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTransmitterEnergy harvestingRayleigh fadingEnergy (signal processing)WirelessElectronic engineeringTransmitter power outputComputer scienceFadingAdditive white Gaussian noiseElectrical engineeringEngineeringTelecommunicationsChannel (broadcasting)Mathematics

Abstract

fetched live from OpenAlex

Energy harvesting is an increasingly attractive source of power for wireless communications devices. In this paper, we consider a point-to-point (P2P) wireless communications system, where both the practical transmitter and receiver, whose hardware circuits consume non-zero power when active, are powered solely by the energy harvested from external sources. An energy cooperation save-then-transmit (EC-ST) scheme is proposed: both the transmitter and receiver go into sleep mode to save energy for a proportion of time while their passive energy harvester units collect energy for later operations, and then become active to communicate for the remaining time proportion (referred to as the active-ratio), during which energy is allowed to flow between the transmitter and receiver. We first consider additive white Gaussian noise one-way channels with two-way energy transfer under a deterministic energy arrival rate. In this case, the optimal active-ratio and the energy cooperation power are obtained in closed form to achieve the maximum throughput. Next, for Rayleigh block fading channels with a stochastic energy arrival rate, we find the optimal energy cooperation power for minimizing the outage probability. Finally, numerical and simulation results are presented to validate the analytical findings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.237
Teacher spread0.196 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations33
Published2015
Admission routes1
Has abstractyes

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