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Record W2597820188 · doi:10.1109/isspit.2016.7886026

Secure performance analysis of satellite communication networks in Shadowed Rician Channel

2016· article· en· W2597820188 on OpenAlexaff
Kefeng Guo, Bangning Zhang, Yuzhen Huang, Daoxing Guo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsSecrecyComputer scienceCorrectnessRician fadingChannel (broadcasting)Communications satelliteComputer networkSatelliteComputer securityAlgorithmFadingEngineering

Abstract

fetched live from OpenAlex

In this paper, we analyze the secure performance of satellite communication networks in Shadowed Rician Channel. It is very important to analyze the secure performance of the satellite-terrestrial networks. In this paper, both the Alice, Bob and Eve are equipped with single antenna respectively. Specifically, the exact closed-form expressions for the probability of non-zero secrecy capacity, the secrecy outage probability and the average secrecy capacity are derived, which provide fast means to evaluate the system performance. In addition, the channel we assume is shadowed Rician (SR) channel, which is close to the reality. Finally, simulation results are provided to verify the correctness of the analytical results.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.334

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 integrity0.0000.000
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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