Holograms in Optical Wireless Communications
Bibliographic record
Abstract
Adaptive beam steering in optical wireless communication (OWC) system has been shown to offer performance enhancements over traditional OWC systems. However, an increase in the computational cost is incurred. In this chapter, we introduce a fast hologram selection technique to speed up the adaptation process. We propose a fast delay, angle and power adaptive holograms (FDAPA-Holograms) approach based on a divide and conquer methodology and evaluate it with angle diversity receivers in a mobile optical wireless (OW) system. The fast and efficient fully adaptive FDAPA-Holograms system can improve the receiver signal to noise ratio (SNR) and reduce the required time to estimate the position of the receiver. The adaptation techniques (angle, power and delay) offer a degree of freedom in the system design. The proposed system FDAPA-Holograms is able to achieve high data rate of 5 Gb/s with full mobility. Simulation results show that the proposed 5 Gb/s FDAPA-Holograms achieves around 13 dB SNR under mobility and under eye safety regulations. Furthermore, a fast divide and conquer search algorithm is introduced to find the optimum hologram as well as to reduce the computation time. The proposed system (FDAPA-Holograms) reduces the computation time required to find the best hologram location from 64 ms using conventional adaptive system to around 14 ms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".