3D-printed actuator-based beam-steering approach for improved physical layer security in visible light communication
Bibliographic record
Abstract
In this study, we present the design, manufacture, and implementation of a 3D-printed lens scanner-based beam steering for use in visible light communication (VLC) applications. The 5cm×5cm scanner is designed for low-cost 3D printing with fused deposition modeling using polylactic acid. Scanning is facilitated through electromagnetic actuation of the lens frame, carrying a conventional 25 mm lens, from two nearly orthogonal directions. The serpentine spring that connects the lens frame to the external frame is tailored to offer similar spring constants in the directions of actuation and has minimal ( <1.5mm ) sag due to the mass of the lens. The manufactured actuator was integrated on a miniaturized VLC test bed ( 70cm×40cm×40cm ). Using the test bed, we characterized the applied voltage versus beam displacement behavior of the actuator in the lateral plane and demonstrated beam steering on a moving target with face-recognition feedback. The proposed scheme was targeted to offer an improved security measure in VLC through tracking the legitimate receiver (i.e., via face recognition) and uses the feedback to steer the focused light onto the targeted device. The joint use of focusing and steering features allows for the legitimate receiver to roam within the room while enjoying the improved secrecy due to the focused light. We calculate the secrecy capacity for the demonstrated approach, which compares favorably with a number of jamming, spatial modulation, and beam-forming counterparts. The presented actuator can be used with larger room dimensions, yet upscaling to larger illumination units will require the use of a lens having smaller focus to address a larger total steering angle.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".