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Record W3186847136 · doi:10.1109/access.2021.3096834

A Dual-Band Ambient Energy Harvesting Rectenna Design for Wireless Power Communications

2021· article· en· W3186847136 on OpenAlexaff
Surajo Muhammad, Jun Jiat Tiang, Sew Kin Wong, Amor Smida, Ridha Ghayoula, Amjad Iqbal

Bibliographic record

VenueIEEE Access · 2021
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRectennaMulti-band deviceRadio frequencyRectifier (neural networks)Impedance matchingElectrical engineeringEnergy harvestingAntenna (radio)GSMElectronic engineeringRF power amplifierComputer sciencePower (physics)WirelessVoltageElectrical impedanceEngineeringTelecommunicationsPhysicsRectification

Abstract

fetched live from OpenAlex

In this paper, a long-range dual-band rectenna for harvesting ambient radio frequency (RF) energy from GSM/900 and GSM/1800 is presented. Theoretical analysis of the proposed dual-band impedance matching network (IMN) is conducted using a modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Pi $ </tex-math></inline-formula> –section matching network (MN). The RF-rectifier is integrated with a dual-band inverted-F monopole antenna. The rectenna circuit complexity is minimized by introducing a dual-band IMN, which plays a significant role in improving the harvester RF-to-dc power conversion efficiency (PCE). Measurement results of the proposed design achieved a peak RF-to-dc PCE of 12.93% and 8.0% for an input power of −30 dBm at 0.9 GHz, and 1.8 GHz, respectively. The RF harvester ambiance measurement attained an output dc voltage of 0.374 V. The circuit generates 0.747 V using a low-powered bq25504-674 evaluation module (EVM). Thus, adequate energy management of the proposed rectenna can be used to power many low-powered devices from the harvested ambient RF energy.

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: Empirical · Consensus signal: none
Teacher disagreement score0.883
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.282
Teacher spread0.227 · 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
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

Citations81
Published2021
Admission routes1
Has abstractyes

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