Pattern synthesis of massive LED arrays for secure visible light communication links
Bibliographic record
Abstract
We propose an indoor multiple-input, single-output (MISO) visible light communication (VLC) system involving massive, i.e., very large, number of light-emitting diodes (LEDs). Thousands of low-intensity LEDs are arranged in a two-dimensional array occupying the entire area of the ceiling to provide uniform illumination. We exploit the excessive spatial degrees of freedom offered by the large number of LEDs to achieve secure communications to the intended receivers, at the physical layer, without precise information about the location or channel gain of potential eavesdroppers. We design the weights (magnitude and sign) of the array elements, i.e., LEDs, to shape the overall pattern of the array and steer its main lobe(s) towards the intended receiver(s), while achieving arbitrarily small signal levels everywhere else inside the room. We formulate the pattern synthesis problem as a linear program with moderate complexity, and characterize the worst-case secrecy rates. We verify the illumination as well as the communication performance of the proposed setup via numerical results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".