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Record W3033429808 · doi:10.1109/tvt.2020.2999752

Performance Analysis of Hybrid Satellite-Terrestrial Cooperative Networks With Relay Selection

2020· article· en· W3033429808 on OpenAlexaff
Kefeng Guo, Min Lin, Bangning Zhang, Jun-Bo Wang, Yongpeng Wu, Wei‐Ping Zhu, Julian Cheng

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaConcordia University
FundersNational Natural Science Foundation of China
KeywordsRelayTelecommunications linkComputer scienceThroughputInterference (communication)Diversity gainSelection (genetic algorithm)Communications satelliteSignal-to-noise ratio (imaging)Cooperative diversityTransmission (telecommunications)Electronic engineeringSatelliteComputer networkTelecommunicationsEngineeringFadingWirelessPower (physics)Decoding methods

Abstract

fetched live from OpenAlex

This paper conducts the performance analysis of a hybrid satellite-terrestrial cooperative network (HSTCN) having multi-antenna terrestrial relays. By assuming the availability of the direct link between the satellite and the destination, and considering the effects of hardware impairments (HIs) and interference on both relays and destination, we first propose a relay selection scheme to enhance the quality of the downlink transmission of the considered HSTCN. Then, we derive analytical expressions for the outage probability (OP) and throughput of the system using the proposed relay selection scheme. Furthermore, the asymptotic behavior of the HSTCN at high signal-to-noise-ratio (SNR) is also investigated to reveal the diversity order and array gain of the system. Finally, computer simulation results are provided to show the effectiveness of the relay selection scheme. The analysis shows that the system performance is significantly affected by the HIs and interference.

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)0.001
metaresearch head score (Gemma)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.210
Teacher spread0.196 · 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

Citations113
Published2020
Admission routes1
Has abstractyes

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