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Record W4404914438 · doi:10.1109/tgcn.2024.3509989

Performance Analysis of Cooperative Wireless-Powered NOMA System With Energy Buffer

2024· article· en· W4404914438 on OpenAlexaff
Kengyuan Xie, Guofa Cai, Ting Ning, Jiguang He, Georges Kaddoum

Bibliographic record

VenueIEEE Transactions on Green Communications and Networking · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversité du Québec à Montréal
FundersKey Laboratory of Industrial Internet of Things and Networked Control, Ministry of EducationNational Natural Science Foundation of China
KeywordsNomaBuffer (optical fiber)WirelessComputer scienceEnergy (signal processing)Computer networkTelecommunicationsTelecommunications linkMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper proposes an energy buffer aided cooperative wireless-powered non-orthogonal multiple access (NOMA) system, which includes a base station (BS), a near user (NU), and a far user (FU). To consider a more general case and improve the system performance as much as possible, both the NU and FU are equipped with energy buffers. Two different energy management policies are considered at the NU and FU, namely the best-effort policy (BEP) and on-off policy (OOP). In the proposed system, the NU and FU first collect and store radio frequency based energy from the BS. Then, the FU uses the stored energy to transmit its signal to both the BS and NU. The NU decodes the received signal from the FU and uses the stored energy to transmit the superimposed signal that consists of its own signal and the decoded FU signal to the BS. The outage probability and throughput expressions of the proposed system with BEP and OOP are derived over Rayleigh fading channels. Results reveal that the proposed system can significantly improve the performance of FU compared to the energy buffer aided non-cooperative wireless-powered NOMA system, and can outperform the buffer-less cooperative wireless-powered NOMA system.

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: none
Teacher disagreement score0.958
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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