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Record W2344901047 · doi:10.1109/tvt.2016.2519893

Outage Performance of Cognitive Hybrid Satellite–Terrestrial Networks With Interference Constraint

2016· article· en· W2344901047 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsConcordia University
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsInterference (communication)Constraint (computer-aided design)Communications satelliteOutage probabilityComputer scienceSatelliteDiversity gainCoding (social sciences)Signal-to-noise ratio (imaging)Topology (electrical circuits)TelecommunicationsMathematicsFadingEngineeringDecoding methodsElectrical engineeringStatistics

Abstract

fetched live from OpenAlex

This paper investigates the performance of a cognitive hybrid satellite-terrestrial network, where the primary satellite communication network and the secondary terrestrial mobile network coexist, provided that the interference temperature constraint is satisfied. By using the Meijer-G functions, the exact closed-form expression of the outage probability (OP) for the secondary network is first derived. Then, the asymptotic result in a high-signal-to-noise-ratio (SNR) regime is presented to reveal the diversity order and coding gain of the considered system. Finally, computer simulations are carried out to confirm the theoretical results and reveal that a more loose interference constraint or heavier shadowing severity of a satellite interference link leads to a reduced OP, whereas stronger satellite interference power poses a detrimental effect on the outage performance.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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