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Record W3201884940 · doi:10.1109/jiot.2021.3116208

An Integrated RF-Powered Wake-Up Wireless Transceiver With –26 dBm Sensitivity

2021· article· en· W3201884940 on OpenAlexaff
Mohammad Amin Karami, Kambiz Moez

Bibliographic record

VenueIEEE Internet of Things Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransceiverTransmitterAmplifierCMOSElectrical engineeringEnvelope detectorRadio frequencyRF power amplifierSensitivity (control systems)dBmElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

This article presents a fully RF-powered wireless transceiver integrating an efficient RF energy harvester (RFEH), a Wake-up Receiver (WuRx), and a Wake-up Transmitter (WuTx) on a single CMOS chip. The WuRx is designed to operate with supply voltages as low as 300 mV allowing to be entirely powered up by the RF energy at power levels as low as −26 dBm. The capability of the WuRx to operate without using a power management unit (PMU) enhances the sensitivity and overall conversion efficiency of the RFEH system. By utilizing an ultralow-power ultralow-voltage envelope detector to obtain the required signal levels, using passive amplification instead of an active low-noise amplifier, and eliminating the voltage regulator removing its power overhead, the transceiver’s input sensitivity has been improved at least by a factor 2 (3 dB) compared to the other previously reported RF-powered transceivers. The proposed transmitter consists of a fast start-up oscillator and an efficient class E power amplifier, which can be externally tuned for different output powers. Fabricated in the TSMC’s 130-nm CMOS process, the measurement results show that the proposed WuRx consuming only 5.7 nW works with input powers as low as −26 dBm, and the proposed transmitter can work with input powers as low as −23 dBm. The WuTx outputs −11 dBm with 51% efficiency at 2.45 GHz using high-Q off-chip components.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.210
Teacher spread0.202 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

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