Securing Cognitive Radio Networks via Relay and Jammer-Based Energy Harvesting on Cascaded Channels
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".