High‐Fidelity Information Transmission Through the Turbulent Atmosphere Utilizing Partially Coherent Cylindrical Vector Beams
Bibliographic record
Abstract
As the demand for high‐capacity and high‐fidelity communication systems continues to increase, addressing the challenges posed by noise and atmospheric turbulence disturbances is imperative. This study introduces and experimentally implements a novel free‐space optical communication protocol. This protocol combines the advantages of reducing the spatial coherence of light at the source with the capabilities of convolutional neural networks at the receiver to encode and transmit optical images through a noisy link. Light beams that are robust against noise are generated and atmospheric turbulence is modeled in a laboratory setting by decreasing the degree of spatial coherence of the source. Eight orbital angular momentum states, four polarizations, and eight coherence states of a light source that generates partially coherent cylindrical vector beams are utilized. These elements are employed to achieve a 256‐ary encoding/decoding data transmission within our protocol. This study is expected to catalyze further research into the utilization of partially coherent light and neural networks in the realm of free‐space optical communications.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".