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Record W4210380051 · doi:10.1109/lwc.2022.3146251

Performance Analysis for Rate Splitting Uplink NOMA Transmission in High Throughput Satellite Systems

2022· article· en· W4210380051 on OpenAlexaff
Huaicong Kong, Min Lin, Zining Wang, Jin‐Yuan Wang, Wei‐Ping Zhu, Jiangzhou Wang

Bibliographic record

VenueIEEE Wireless Communications Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsConcordia University
FundersNanjing University of Posts and Telecommunications
KeywordsTelecommunications linkRician fadingNomaComputer scienceThroughputFadingTransmission (telecommunications)Channel state informationSpectral efficiencyBeamformingCommunications satelliteBit error rateElectronic engineeringChannel (broadcasting)Computer networkSatelliteTelecommunicationsWirelessEngineering

Abstract

fetched live from OpenAlex

This letter investigates the return link performance of a high throughput satellite system, where the feeder link uses free-space optical technology while the user link employs radio frequency transmission. Based on the statistical channel state information, we first propose a scheme for combining rate splitting uplink non-orthogonal multiple access with beamforming in the user link to improve the spectral efficiency. Then, by assuming that the user link undergoes the shadowed-Rician fading while the feeder link experiences Málaga turbulence fading with nonzero-boresight pointing error, we derive a closed-form average throughput expression of the system with the proposed scheme. Finally, simulation results are provided to demonstrate the correctness of our theoretical analysis and the superiority of our proposed scheme to some previous works.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.033
GPT teacher head0.249
Teacher spread0.216 · 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.

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

Citations23
Published2022
Admission routes1
Has abstractyes

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