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Record W3160082277 · doi:10.1002/sat.1401

Performance analysis for the forward link of multiuser satellite communication systems

2021· article· en· W3160082277 on OpenAlexaff
Yu-Qing Guo, Huaicong Kong, Qingquan Huang, Min Lin, Wei‐Ping Zhu, Hamidreza Amindavar

Bibliographic record

VenueInternational Journal of Satellite Communications and Networking · 2021
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsConcordia University
FundersShanghai Aerospace Science and Technology Innovation Foundation
KeywordsComputer scienceFadingCommunications satelliteKu bandSatelliteScheduling (production processes)Link budgetTelecommunicationsErgodic theoryPath lossComputer networkReal-time computingWirelessDecoding methodsMathematicsMathematical optimization

Abstract

fetched live from OpenAlex

Summary Broad‐band satellite communication is an indispensable method to provide seamless connectivity for people especially in remote areas. This paper analyzes the performance of the forward link of a multiuser satellite system operating in Ka band, where a gateway delivers signal to a satellite that forwards the amplified signal to multiple users on the ground. By assuming that satellite links experience double lognormal fading due to rain attenuation and taking the effects of antenna pattern and path loss into account, we first exploit the opportunistic user scheduling (OUS) scheme to obtain the maximal output signal‐to‐noise ratio (SNR) expression of the satellite system. Then, the analytical expressions for both outage probability (OP) and ergodic capacity (EC) of the considered satellite system are derived. Finally, simulation results demonstrate the validity of the theoretical analysis and the superiority of the OUS scheme in comparison with round‐robin scheme.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.282
Teacher spread0.239 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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