Cascaded κ-µ Fading Channels with Colluding Eavesdroppers: Physical-Layer Security Analysis
Bibliographic record
Abstract
This paper studies the physical-layer security (PLS) of a system model consisting of a transmitter, a receiver, and multiple eavesdroppers. Cascaded general fading channel, which is the κ-μ distribution is assumed at the main and the wiretap links of the network. The impacts of the cascade level, the number of eavesdroppers attempting to overhear the confidential information, and the wiretap channel's parameters on the system's secrecy are investigated. Two of the main secrecy metrics are used to evaluate the secrecy level of the system, which are the secrecy outage probability (OP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sec</sub> ) and the probability of non-zero secrecy capacity (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nsc</sub> ). Exact and asymptotic form expressions for (OP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sec</sub> ) and (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nsc</sub> ) are derived. Asymptotic analysis is performed to gain a clear vision about the impact of some key parameters over the secrecy. The results show that the fading channel cascade level has a significant effect on the system's secrecy. Also, the results show that the system is less protected when increasing the number of eavesdroppers or when improving the wiretap channel's conditions. Analytical results are validated using Monte-Carlo simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".