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Record W2590097296 · doi:10.1002/ett.3144

Optimal power allocation for relay‐based cooperative communication systems with energy harvesting

2017· article· en· W2590097296 on OpenAlexaff
Marzieh Ajirak, Mohammad Javad Omidi, Hamid Saeedi‐Sourck, Arin Minasian

Bibliographic record

VenueTransactions on Emerging Telecommunications Technologies · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelayMathematical optimizationEnergy (signal processing)Computer scienceMaximizationMarkov decision processChannel (broadcasting)Optimization problemEnergy harvestingConvex optimizationCommunications systemNode (physics)ThroughputGaussianPower (physics)Markov processRegular polygonWirelessComputer networkTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, the problem of throughput maximization in cooperative communication systems is considered, in which the nodes use energy harvesters instead of conventional energy sources. The 3‐node Gaussian relay channel with decode‐and‐forward relaying scheme is investigated, where the source and relay harvest energy from the environment. In this work, optimal power levels for 2 cases are obtained. First, a deterministic model is considered, where the noncausal (offline) knowledge of the harvested energy and channel states is available. The problem is shown to be a convex optimization problem, where the solutions to which are derived using convex optimization techniques. Then, the online case is investigated, in which only the casual knowledge of the harvested energy and channel states are used. In the online setting, the problem is formulated as a Markov decision process. The performance of the system in both offline and online cases, along with a few suboptimal schemes introduced in the literature, is evaluated using computer simulations.

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), Science and technology studies
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 score0.833
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
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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