Pattern synthesis of massive LED arrays for secure visible light communication links
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it