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Record W3034389670 · doi:10.1186/s13638-020-01732-2

Green communication for MIMO SWIPT-powered 5G Internet of Things with full-duplex relay base on secure transmission and energy collection constraints

2020· article· en· W3034389670 on OpenAlexaff
Meng Zhang, Qian Huang, Michel Kadoch

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsÉcole de Technologie Supérieure
FundersNational Science and Technology Major Project
KeywordsComputer scienceMathematical optimizationOptimization problemRelayBase stationTransmitterTransmitter power outputMIMOTransmission (telecommunications)Node (physics)Convex optimizationArtificial noisePower (physics)AlgorithmTelecommunicationsRegular polygonBeamformingMathematics

Abstract

fetched live from OpenAlex

Abstract The green communication for 5th generation Internet of Things base on the energy collecting and security issues is studied in this paper. The transceiver and power splitting factor are optimized aiming at minimizing the transmitting power at the source node subject to the security performance and energy collection. Since the optimization problem is a nonconvex function, an iteration optimization algorithm is proposed to divide the objective optimization function into two subproblems, namely, a joint transmitter and PS factor optimization subproblem and a minimum mean-square error-based receiving filter optimization subproblem. Furthermore, since the transmitter and PS combining optimization subproblem is still a nonconvex function, the semidefinite relaxation technique is used to transform it into a convex function. The feasibility of the optimization problem and the convergency of the iteration algorithm are proved in theory, and the simulation results show the availability of the proposed scheme.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.993

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 integrity0.0000.001
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 head0.023
GPT teacher head0.224
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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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