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Record W2539200098 · doi:10.1109/tcomm.2016.2619707

Design of Simultaneous Wireless Information and Power Transfer Scheme for Short Reference DCSK Communication Systems

2016· article· en· W2539200098 on OpenAlexafffund
Georges Kaddoum, Ha-Vu Tran, Long Kong, Michael Atallah

Bibliographic record

VenueIEEE Transactions on Communications · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceChannel (broadcasting)Rayleigh fadingEnergy (signal processing)Maximum power transfer theoremWirelessBit error rateElectronic engineeringTransmission (telecommunications)Data transmissionPower (physics)FadingTelecommunicationsComputer networkEngineeringMathematics

Abstract

fetched live from OpenAlex

Recently, a short reference differential chaos shift keying system (SR-DCSK) has been proposed to overcome the dominant drawbacks related to low data rate and energy efficiency fondness of conventional DCSK systems. The fact that terminals on a network have a limited battery capacity and are in desperate need to high energy efficiency transmission schemes compels us to tackle these crucial challenges. In this paper, we propose an SR-DCSK system that performs simultaneous wireless information and power transfer (SWIPT). This promising design exploits the saved time gained from the fact that reference signal duration of SR-DCSK scheme occupies less than half of the bit duration to transmit a signal. The aim of this system is to allow receivers to perform without being equipped with any external power supply. Furthermore, at the receiver side, an RF-to-dc conversion is first performed, followed by data recovery without the need to any channel estimator. Closed-form expressions of multiple-input single-output SR-DCSK SWIPT system, such as ergodic rate, harvesting time, energy shortage, and data outage as well as exact and approximate bit error rate probabilities are derived under Rayleigh fading channel and are validated via simulation. Our results show that the proposed solution saves energy without sacrificing the non-coherent fashion of the system or reducing the rate compared to conventional DCSK, while keeping the design simple.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.241
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations82
Published2016
Admission routes2
Has abstractyes

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