MétaCan
Menu
Back to cohort

Securing Cognitive Radio Networks via Relay and Jammer-Based Energy Harvesting on Cascaded Channels

2023· article· en· W4387883697 on OpenAlex
Deemah H. Tashman, Walaa Hamouda, Iyad Dayoub

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsRelayJammingComputer networkUnderlayCognitive radioComputer scienceEnergy harvestingRayleigh fadingTransmitterFadingWirelessEnergy (signal processing)SecrecyPhysical layerExploitChannel (broadcasting)TelecommunicationsPower (physics)Signal-to-noise ratio (imaging)Computer securityMathematicsPhysics

Abstract

fetched live from OpenAlex

Physical-layer security (PLS) is examined in this paper for an underlay cognitive radio network (CRN). Two secondary users (SUs) interact through a relay that is equipped with multiple antennas and harvests energy from the SU transmitter's messages via a power splitting (PS) approach. Communication between the relay and SU destination is being intercepted by several eavesdroppers. Therefore, to diminish the eavesdroppers' interception capabilities, the SU destination gathers energy from relayed messages and exploits it to generate and broadcast jamming signals intended to mislead the eavesdroppers. Colluding and non-colluding eavesdroppers are both considered and contrasted as possible strategies for intercepting private information. Additionally, for a more realistic assumption, the connection between the relay and the legitimate SU receiver is assumed to follow the cascaded Rayleigh fading model. PLS is assessed in terms of the probability of non-zero secrecy capacity and the intercept probability.

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 score0.502
Threshold uncertainty score0.842

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 science0.0000.000
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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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