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Record W2601472403 · doi:10.1109/vtcfall.2016.7881217

Secrecy Performance of Dual-Hop Threshold Relaying System with Diversity Reception

2016· preprint· en· W2601472403 on OpenAlexaff
Chinmoy Kundu, Telex M. N. Ngatched, Octavia A. Dobre

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRelaySecrecyComputer scienceErgodic theoryChannel state informationSignal-to-noise ratio (imaging)Computer networkMaximal-ratio combiningHop (telecommunications)Diversity gainChannel (broadcasting)Outage probabilityWirelessTopology (electrical circuits)TelecommunicationsFadingMathematicsPhysicsElectrical engineeringComputer securityEngineeringPower (physics)

Abstract

fetched live from OpenAlex

In this paper, the secrecy of a cooperative system consisting of a single source, relay, destination and eavesdropper is analyzed. The threshold-selection decode-and-forward relay is considered, where the relay can correctly decode and forward only if it satisfies a threshold signal-to-noise ratio (SNR). Both destination and eavesdropper take advantage of the direct and relayed transmissions through maximal ratio diversity combining. The secrecy outage probability (SOP) and ergodic secrecy rate are derived in closed-form for different channel state information (CSI) availability. It was observed that when the required rate is low, having CSI knowledge is more advantageous than in the case of higher rate. An increase in the required threshold SNR at the relay can increase the SOP if the relayed link SNR is relatively higher than the direct link SNR. It was also shown that SOP cannot be improved beyond a certain value when keeping either dual-hop link average SNR fixed and increasing the other link SNR, whereas the ergodic secrecy rate can be increased by keeping the source to destination average SNR fixed.

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 score0.450
Threshold uncertainty score0.796

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 science0.0010.002
Research integrity0.0000.001
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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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