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The Concept of Time Sharing NOMA into UAV-Enabled Communications: An Energy-Efficient Approach

2020· article· en· W3089590586 on OpenAlexaff
Antonino Masaracchia, Long D. Nguyen, Cheng Yin, Octavia A. Dobre, Emiliano Garcia‐Palacios

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTelecommunications linkComputer scienceEfficient energy useNomaContext (archaeology)Quality of serviceThroughputTransmitter power outputPower (physics)Computer networkEnergy (signal processing)TransmitterDistributed computingReal-time computingWirelessTelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a framework for the optimal power allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of large-scale scenario. The objective of this proposed framework is to maximize the energy efficiency (EE) within the UAV communication range. The idea behind is to propose a communication system that merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm maximizing the downlink EE among users. The resulting model finds applicability in performing energy efficient transmissions into power-constrained communication scenarios such as in disaster communications. Performance investigations regarding the proposed framework show its capability in finding the optimal energy efficient configuration of power resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. Furthermore, the proposed framework resulted able to maintain a good level of throughput fairness among users in downlink.

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 score0.959
Threshold uncertainty score0.222

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 head0.014
GPT teacher head0.208
Teacher spread0.193 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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