Intercept probability analysis of cooperative wireless networks with best relay selection in the presence of eavesdropping attack
Bibliographic record
Abstract
Due to the broadcast nature of wireless medium, wireless communication is extremely vulnerable to eavesdropping attack. Physical-layer security is emerging as a new paradigm to prevent the eavesdropper from interception by exploiting the physical characteristics of wireless channels, which has recently attracted a lot of research attentions. In this paper, we consider the physical-layer security in cooperative wireless networks with multiple decode-and-forward (DF) relays and investigate the best relay selection in the presence of eavesdropping attack. For the comparison purpose, we also examine the conventional direct transmission without relay and traditional max-min relay selection. We derive closed-form intercept probability expressions of the direct transmission, traditional max-min relay selection, and proposed best relay selection schemes in Rayleigh fading channels. Numerical results show that the proposed best relay selection scheme strictly outperforms the traditional direct transmission and max-min relay selection schemes in terms of intercept probability. In addition, as the number of relays increases, the intercept probabilities of both traditional max-min relay selection and proposed best relay selection schemes decrease significantly, showing the advantage of exploiting multiple relays against eavesdropping attack.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".