Analyzing Physical-Layer Security of PLC Systems Using DCSK: A Copula-Based Approach
Bibliographic record
Abstract
This study analyzes the physical layer security (PLS) performance of a differential chaos shift keying (DCSK) modulation-based Power Line Communication (PLC) system by exploiting the novel Farlie-Gumbel-Morgenstern (FGM) Copula approach. A power line Wyner’s wiretap channel model is investigated, where the main channel and the wiretap channel are assumed to be correlated and Log-normally distributed. The Gamma approximation to the Log-normal distribution is employed to simplify the computation. Concurrently, the PLC channel noise is modeled as a Bernoulli-Gaussian random process. Utilizing a Copula based approach to model the dependence among the correlated PLC channels, the PLS performance of the PLC system is evaluated in terms of the secure outage probability (SOP) and the strictly positive secrecy capacity (SPSC). It is revealed through the asymptotic SOP analysis that the secrecy diversity order depends on the shaping parameter$(m_{\gamma _{M}})$of the main channel. We also propose an algorithm to maximize the secrecy throughput under SOP constraints. Based on the insights from this analysis, it has been seen that the SOP performance degrades when the value of the dependence parameter$(\theta)$increases. Also, the secrecy throughput performance improves with a lower optimal threshold value of the signal-to-noise ratio (SNR),$\gamma _{\textrm {th}}$. Furthermore, some other insightful observations are presented by studying the impact of different parameters such as spreading factor$(\beta)$, impulsive noise occurrence probability$(p)$, transmitted power$(P_{T})$, and impulsive noise index$(K)$.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".