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Record W2175451653 · doi:10.1109/pacrim.2015.7334844

Secrecy capacity and wireless energy harvesting in amplify-and-forward relay networks

2015· article· en· W2175451653 on OpenAlexaff
Dan Su, S. Ali Mousavifar, Cyril Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRelayJammingSecrecyComputer networkComputer scienceWirelessEnergy harvestingRelay channelEnergy (signal processing)Power (physics)TelecommunicationsComputer securityMathematicsPhysics

Abstract

fetched live from OpenAlex

The secrecy capacity (SC) from the source to a destination in a dual-hop amplify-and-forward (AF) relay network using cooperative jamming (CJ) is investigated. Conditions for achieving a positive SC are derived. An energy constrained untrusted cooperative relaying protocol is considered in which the relay forwards information from the source to the destination while harvesting energy from the source signal and the destination jamming signal. An optimization problem is proposed to maximize the SC subject to the energy constraints. A method is proposed to find the optimal power split ratio (PSR) which maximizes the SC. An approximate expression for the optimal PSR is also derived. Results show that a mutually beneficial relationship exists between source-destination pair and the relay. This allows the relay to achieve a net energy gain while the secrecy of the confidential message from the source to the destination is protected.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.002
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.218
Teacher spread0.190 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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