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Record W2587251524 · doi:10.1109/glocomw.2016.7849084

Widely Linear Multiuser Simultaneous Information and Power Transfer with One-Dimensional Signaling

2016· article· en· W2587251524 on OpenAlexaff
Majid Bavand, Steven D. Blostein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsPrecodingBeamformingComputer scienceTelecommunications linkDecoding methodsWirelessMaximum power transfer theoremMinimum mean square errorTransmission (telecommunications)Transmitter power outputChannel (broadcasting)Electronic engineeringPower (physics)AlgorithmComputer networkTransmitterTelecommunicationsMIMOMathematicsEngineeringEstimator

Abstract

fetched live from OpenAlex

Emerging wireless technologies require the support for a massive number of low data rate battery operated devices. With this motivation, this paper employs widely linear processing in simultaneous wireless information and power transfer (SWIPT) systems. By considering the transmission of low-rate one-dimensionally modulated signals in the downlink of a SWIPT broadcast channel, a widely linear zero forcing (ZF) precoder and a widely linear minimum mean square error (MMSE) precoder are proposed which are capable of offering service to more users than the number of transmit antennas. Semi closed-form solutions are obtained for both scenarios and iterative dual ascent based algorithms are presented to obtain the transmit precoding (beamforming) matrices. Simulation results demonstrate the effect of widely linear processing in increasing the number of information decoding 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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207