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Record W3020160405 · doi:10.1109/lwc.2020.2990337

Secrecy Outage Performance of Ground-to-Air Communications With Multiple Aerial Eavesdroppers and Its Deep Learning Evaluation

2020· article· en· W3020160405 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 Wireless Communications Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of Victoria
FundersQatar University
KeywordsSecrecyComputer scienceBeamformingFadingBase stationTransmission (telecommunications)Channel (broadcasting)ComputationWirelessStochastic geometryData transmissionComputer networkReal-time computingTelecommunicationsComputer securityAlgorithmStatistics

Abstract

fetched live from OpenAlex

In this letter, we study the secure information transmission from a ground base station (GBS) to a legitimate unmanned aerial vehicle (UAV) user, in the presence of multiple UAV eavesdroppers. To enhance the secrecy performance, the GBS applies beamforming transmission while enforcing a protection zone around it. Utilizing the general κ-μ shadowed fading distribution to model the ground-to-air channel, we derive an exact expression of the secrecy outage probability (SOP). To further facilitate performance evaluation, we adopt a data-driven approach and develop a deep learning model that can predict the SOP performance with high accuracy and short computation time. Through selected numerical results, we examine the effect of different system parameters on the SOP 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.821

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 science0.0010.000
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 head0.026
GPT teacher head0.236
Teacher spread0.210 · 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