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Record W6920287107 · doi:10.60692/tg7nc-fkb53

Effects of CSI Knowledge on Secrecy of Threshold-Selection Decode-and-Forward Relaying

2017· article· en· W6920287107 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2017
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSecrecyRelayNode (physics)BottleneckChannel state informationChannel (broadcasting)Information-theoretic securityWireless

Abstract

fetched live from OpenAlex

This paper considers secrecy of a three node cooperative wireless system in the presence of a passive eavesdropper. The threshold-selection decode-and-forward relay is considered, which can decode the source message correctly only if a predefined signal-to-noise ratio (SNR) is achieved. The effects of channel state information (CSI) availability on secrecy outage probability (SOP) and ergodic secrecy rate (ESR) are investigated, and closed-form expressions are derived. Diversity is achieved from the direct and relaying paths both at the destination and at the eavesdropper by combinations of maximal-ratio combining and selection combining schemes. An asymptotic analysis is provided when each hop SNR is the same in the balanced case and when it is different in the unbalanced case. The analysis shows that both hops can be a bottleneck for secure communication; however, they do not affect the secrecy identically. While it is observed that CSI knowledge can improve secrecy, the amount of improvement for SOP is more when the required rate is low and for ESR when the operating SNR is also low. It is also shown that the source to eavesdropper link SNR is more crucial for secure communication.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.512

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 communication0.0000.001
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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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