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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(m_{\gamma _{M}})$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\theta)$ </tex-math></inline-formula> increases. Also, the secrecy throughput performance improves with a lower optimal threshold value of the signal-to-noise ratio (SNR), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma _{\textrm {th}}$ </tex-math></inline-formula> . Furthermore, some other insightful observations are presented by studying the impact of different parameters such as spreading factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\beta)$ </tex-math></inline-formula> , impulsive noise occurrence probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(p)$ </tex-math></inline-formula> , transmitted power <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{T})$ </tex-math></inline-formula> , and impulsive noise index <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(K)$ </tex-math></inline-formula> .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".