Physical-Layer Security for MISO Visible Light Communication Channels
Bibliographic record
Abstract
This paper considers improving the confidentiality of visible light communication (VLC) links within the framework of physical-layer security. We study a VLC scenario with one transmitter, one legitimate receiver, and one eavesdropper. The transmitter has multiple light sources, while the legitimate and unauthorized receivers have a single photodetector, each. We characterize secrecy rates achievable via transmit beamforming over the multiple-input, single-output (MISO) VLC wiretap channel. For VLC systems, intensity modulation (IM) via light-emitting diodes (LEDs) is the most practical transmission scheme. Because of the limited dynamic range of typical LEDs, the modulating signal must satisfy certain amplitude constraints. Hence, we begin with deriving lower and upper bounds on the secrecy capacity of the scalar Gaussian wiretap channel subject to amplitude constraints. Then, we utilize beamforming to obtain a closed-form secrecy rate expression for the MISO wiretap channel. Finally, we propose a robust beamforming scheme to consider the scenario wherein information about the eavesdropper's channel is imperfect due to location uncertainty. A typical application of the proposed scheme is to secure the communication link when the eavesdropper is expected to exist within a specified area. The performance is measured in terms of the worst-case secrecy rate guaranteed under all admissible realizations of the eavesdropper's channel.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".