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Record W3085215956 · doi:10.1109/tvt.2020.3025371

Improper Gaussian Signaling for Computationally Tractable Energy and Information Beamforming

2020· article· en· W3085215956 on OpenAlexfundno aff
Hongwen Yu, Hoang Duong Tuan, Ali A. Nasir, Trung Q. Duong, Lajos Hanzo

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilInstitute for Computational Science and TechnologyQueen's UniversityNational Natural Science Foundation of ChinaQueen's University Belfast
KeywordsBeamformingEnergy (signal processing)Decoding methodsGaussianThroughputComputer scienceInformation transferWirelessTransmitter power outputElectronic engineeringPower (physics)Energy harvestingMaximum power transfer theoremWireless power transferAlgorithmEngineeringTelecommunicationsMathematicsChannel (broadcasting)TransmitterPhysics

Abstract

fetched live from OpenAlex

The transmit time-switching (transmit-TS) approach, under which the energy and information are transferred over different fractions of a time slot has proved its supremacy over the power splitting (PS) approach of simultaneous wireless information and power transfer, where PS splits the power of the received signal for energy harvesting and information decoding. For integrating data and energy transfer, this paper develops new classes of beamforming that are suitable for improper Gaussian signaling which is capable of network throughput improvements while maintaining high computational efficiency in its design.

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 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 score0.934
Threshold uncertainty score0.702

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 science0.0000.000
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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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.

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

Citations11
Published2020
Admission routes1
Has abstractyes

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