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Record W3201013425 · doi:10.1109/tgcn.2021.3113674

Fair Scheduling of Wireless Power Transfer to Nonlinear Energy Harvesters

2021· article· en· W3201013425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Green Communications and Networking · 2021
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransmitterComputer scienceWirelessWireless power transferScheduling (production processes)MultiplexingHomogeneousAlgorithmTopology (electrical circuits)MathematicsChannel (broadcasting)TelecommunicationsMathematical optimizationCombinatorics

Abstract

fetched live from OpenAlex

The performances of two wireless power transfer (WPT) scheduling schemes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time sharing</i> (TS) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial multiplexing</i> (SM), in terms of provisioning fairness among energy receivers (ERs), are studied and compared while taking into account the nonlinearity of the harvesting circuits. In the network, the multiple-antenna energy transmitter attempts to maximize the harvested energy by the ER which has accumulated the minimum amount of energy among all single-antenna ERs during the WPT block—hence the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max-min</i> fairness criterion. Two network scenarios are studied: homogeneous, where similar channel coefficients are assumed for all the well-apart ERs, and heterogeneous, where the said coefficients can take arbitrary values. For WPT in single-band or multi-band, we analytically prove that the optimal scheduling policy in the homogeneous scenario is to allocate each ER the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full transmit power</i> with uniform distribution of charging times among ERs, rather than to allocate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full power transfer block</i> with uniform distribution of the power among ERs. Generalization of the network to the heterogeneous scenario aims to find the optimal beamforming vectors for the SM scheme and the optimal time sharing vector for the TS scheme. We form the max-min fairness optimization problems for both scheduling techniques, in single- and multi-band WPT scenarios. The problems, which are nonlinear and non-convex, are solved through exhaustive search algorithms. It is proven that the TS scheduling outperforms the SM one in terms of max-min fairness as a result of taking into account the inherent non-linearity of the harvesting devices.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.892
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 studies0.0000.000
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 head0.018
GPT teacher head0.224
Teacher spread0.206 · 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