On the Design of Artificial Noise for Physical Layer Security in Visible Light Communication Channels With Clipping
Bibliographic record
Abstract
Though visible light communication (VLC) systems are contained to a given room, improving their security is an important criterion in any practical deployment. This paper studies the design of artificial noise (AN) to enhance physical layer security in VLC systems in the context of input signals with no explicit amplitude constraint (e.g., multicarrier systems). In such systems, clipping is needed to constrain the input signals within the limited linear ranges of the LEDs. However, this clipping process gives rise to non-linear clipping distortion, which must be incorporated into the AN design. To solve the design problem, a sub-optimal approach is presented using the Charnes-Cooper transformation and the convex-concave procedure (CCP). Then, a novel AN transmission scheme is proposed to reduce the impact of clipping distortion, thus improving the secrecy performance. The proposed scheme exploits the typical structure of LED luminaries that are composed of multiple light-emitting chips. Specifically, LED chips in each luminaire are divided into two groups driven by separate driver circuits. One group is used to transmit the information-bearing signal, while the other group transmits the AN. Numerical results show that while clipping distortion can significantly reduce the secrecy level, with proper design AN significantly improves the secrecy performance using the proposed design methodology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".