Cascaded <inline-formula> <tex-math notation="LaTeX">$\alpha-\mu$ </tex-math> </inline-formula> Fading Channels: Reliability and Security Analysis
Bibliographic record
Abstract
In this paper, the cascaded α - μ fading distribution is first introduced and mathematically characterized, which arises as a generalization of the cascaded Rayleigh, Weibull, and Nakagami-m fading distribution, by properly selecting fading parameters α and μ with specific values. In particular, the statistical characterization of the cascaded α - μ fading channels, namely, the probability density function and cumulative distribution function, are first studied. This set of new statistical results is applied to the modeling and analysis of the reliability and security performance of wireless communication systems over the cascaded α-μ fading channel. Regarding system reliability, the amount of fading, outage probability, average channel capacity, and the average symbol error probability with coherent and non-coherent demodulation schemes are derived with respect to the univariate Fox's H-function. In terms of security analysis, the secrecy outage probability Pout, the probability of non-zero secrecy capacity Pnz, and the average secrecy capacity are analyzed in the exact closed-form expressions which are derived in the presence of an active eavesdropper. In addition, an asymptotic analysis of all aforementioned metrics is carried out, in order to gain more insights of the effect of the key system parameters on the reliability and security. Tractable results are computed in terms of the Fox's H-function and later on are successfully validated through Monte-Carlo simulations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".