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Record W2736754802 · doi:10.1109/lwc.2017.2729554

Mode Switching for SWIPT Over Fading Channel With Nonlinear Energy Harvesting

2017· article· en· W2736754802 on OpenAlex
Jae‐Mo Kang, Il‐Min Kim, Dong In Kim

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 Wireless Communications Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of Korea
KeywordsFadingEnergy harvestingComputer scienceWirelessDecoding methodsNonlinear systemMaximum power transfer theoremChannel (broadcasting)Energy (signal processing)Information transferElectronic engineeringPower (physics)Computer networkTelecommunicationsMathematicsEngineeringStatisticsPhysics

Abstract

fetched live from OpenAlex

We study mode switching (MS) between information decoding and energy harvesting (EH) for simultaneous wireless information and power transfer (SWIPT) over the fading channel. Unlike the existing result obtained with a simplistic assumption of linear EH, we consider a realistic scenario of the nonlinear EH. In this setting, to design the optimal MS, we address the problem of maximizing the average achievable rate under an average harvested energy constraint, which is generally nonconvex and combinatorial. Using the time-sharing condition, the optimal MS solution for the nonlinear EH is presented efficiently. From the obtained result and further analysis, we draw interesting and important insights into the optimized SWIPT system with nonlinear EH.

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), 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: Empirical · Consensus signal: none
Teacher disagreement score0.511
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 studies0.0020.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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