MétaCan
Menu
Back to cohort
Record W2943221627 · doi:10.1049/el.2019.0707

Secrecy outage analysis of double shadowed Rician channels

2019· article· en· W2943221627 on OpenAlexaff
Yun Ai, Long Kong, Michael Cheffena

Bibliographic record

VenueElectronics Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsRician fadingSecrecyComputer scienceTransmitterLine-of-sightTelecommunicationsElectronic engineeringPhysicsComputer securityChannel (broadcasting)FadingEngineering

Abstract

fetched live from OpenAlex

The double shadowed Rician model was recently proposed to describe some realistic physical signal propagation phenomena, where a Rician faded signal is impacted by cascaded shadowing processes. In this Letter, the authors study the outage performance of the double shadowed Rician model. More specifically, they investigate the impact of two different shadowing processes on the secrecy performance by deriving novel and exact expressions for secrecy outage performance metrics. The obtained results reveal the following physical implications: (i) when a Rician faded signal undergoes line‐of‐sight shadowing, which is further cascaded by another round of composite shadowing, the latter form of shadowing imposes larger impact on the secrecy performance; (ii) the widely investigated concept of ‘protected zone’ to improve the secrecy performance should not be limited to the legitimate transmitter but also extended to the legitimate receiver due to the adverse shadowing effect caused by objects in its vicinity; and (iii) the rate of outage performance degradation grows larger as the shadowings become severe.

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)0.003
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0010.001
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 head0.007
GPT teacher head0.218
Teacher spread0.212 · 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

Citations36
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueElectronics LettersSame topicWireless Communication Security TechniquesFrench-language works237,207