Bibliographic record
Abstract
Despite offering higher security than radio frequency (RF) channels, the broadcast nature of the visible light communication (VLC) channel makes VLC links inherently susceptible to eavesdropping by unauthorized users. In this work, we consider the physical-layer security of VLC links aided by friendly jamming. The jammer has multiple light sources, but does not have access to the data transmitted. The eavesdropper's reception is degraded by a jamming signal that causes no interference to the legitimate receiver. Due to the limited dynamic range of typical light-emitting diodes (LEDs), both the data and jamming signals are subject to amplitude constraints. Therefore, we begin with deriving a closed-form secrecy rate expression for the corresponding wiretap channel, and adopt secrecy rate as the performance measure. Then, we formulate a linear programming problem to maximize the secrecy rate when the eavesdropper's channel is accurately known to the jammer. Finally, we consider robust beamforming to maximize the worst-case secrecy rate when information about the eavesdropper's channel is uncertain due to location uncertainty. The robust scheme makes use of simple linear programming, making real-time implementation feasible in a variety of real-world scenarios.
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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 | 0.002 |
| 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 | 0.000 | 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".