Short-Length Raptor Codes for Mobile Free-Space Optical Channels
Bibliographic record
Abstract
Free-space optical (FSO) links are competitive wireless links offering high data rate, security and low system complexity. For mobile applications, e.g., from a ground base station to an unmanned aerial vehicle (UAV), the FSO channel can suffer from severe instantaneous misalignment. This time varying misalignment is unknown to the transmitter and causes data packet corruption and erasure. As a result, the application of traditional fixed-rate erasure coding techniques is difficult. In this paper, we consider the application of rateless Raptor codes for such mobile FSO channels. Due to the high data rates required, short-length (16-1024) Raptor codes are designed and simulated on a severe jitter FSO channel. A key advantage of Raptor codes is their independence on channel state, no matter how large the misalignment. With a 1 Gbps transmitter, the designed Raptor code with k = 64 message packets offers 560 Mbps data rate and decoding cost of 4.14 operations per packet when transmitting power is 20 dBm. In contrast, a traditional automatic repeat- request (ARQ) algorithm technique on the same FSO jitter channel achieves a rate of 60 Mbps.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".