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Record W2900640669 · doi:10.1109/tvt.2018.2866612

On the Performance of NOMA in the Two-User SWIPT System

2018· article· en· W2900640669 on OpenAlexaff
Mohammadali Hedayati, Il‐Min Kim

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsNomaDecoding methodsComputer scienceWirelessQuality of serviceEnergy consumptionMaximum power transfer theoremChannel (broadcasting)Energy harvestingEnergy (signal processing)Cognitive radioComputer networkPower (physics)TelecommunicationsTelecommunications linkEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, we investigate nonorthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) system consisting of a battery powered access point and two energy harvesting users. In SWIPT, there exists an information-energy tradeoff. Since the adoption of NOMA may cause more energy consumption than the orthogonal multiple access (OMA), it is not known whether NOMA can always enhance the spectral efficiency in SWIPT systems compared to OMA. We prove that NOMA performs better than OMA when the decoding energy consumption is negligible. However, for the nonnegligible decoding energy consumption, we show that NOMA is not always superior to OMA. Interestingly enough, for the nonnegligible decoding energy consumption, OMA can outperform NOMA when the channel power gains of the two users are not sufficiently different. Moreover, we study the performance of cognitive radio inspired NOMA (CR-NOMA), in which the power is allocated to the user with poor channel condition such that its quality of service (QoS) requirement is met. Different from the battery powered CR-NOMA, the analytical results show that CR-NOMA can outperform OMA in SWIPT systems only if the channel power gains of users are sufficiently different and the QoS of the weak user is above a threshold.

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: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.194
Teacher spread0.187 · 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

Citations47
Published2018
Admission routes1
Has abstractyes

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