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Record W2953761555 · doi:10.1109/tvt.2019.2925736

A Novel Energy Harvesting Scheme for Mixed FSO-RF Relaying Systems

2019· article· en· W2953761555 on OpenAlexaff
Jianchao Chen, Liang Yang, Wenjie Wang, Hong‐Chuan Yang, Yuanwei Liu, Mazen O. Hasna, Mohamed‐Slim Alouini

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of Victoria
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory of Advanced Optical Communication Systems and NetworksDepartment of Education of Guangdong ProvinceShanghai Jiao Tong UniversityNational Natural Science Foundation of China
KeywordsEnergy harvestingElectronic engineeringRadio frequencyEnergy (signal processing)RelayCorrectnessMonte Carlo methodComponent (thermodynamics)Computer scienceCommunications systemTopology (electrical circuits)MathematicsAlgorithmPhysicsEngineeringTelecommunicationsElectrical engineeringPower (physics)Statistics

Abstract

fetched live from OpenAlex

In this correspondence paper, we consider the performance of a mixed free-space optical and radio frequency (FSO-RF) relaying system with energy harvesting in the existence of both atmospheric turbulence and pointing errors. More specially, we assume that the direct current component of the FSO signal, which is generally filtered out, can be used for energy harvesting at the relay. We derive the exact closed-form expression for the outage probability of this setup in terms of the bivariate Fox-H function. We also provide the asymptotic analysis to obtain the diversity order. Finally, Monte Carlo simulations are carried out to verify the correctness of our analytical results.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.014
GPT teacher head0.217
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 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

Citations68
Published2019
Admission routes1
Has abstractyes

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