Synodic Period Channel Modeling and Coding Scheme for Deep Space Optical Communications
Bibliographic record
Abstract
In this paper, we design an end-to-end free-space optical (FSO) communication coding scheme for deep space exploration. Take the Mars exploration as an example, we first establish the synodic period FSO channel model by taking account into the solar scintillation, and derive the corresponding upper bound of bit error rate (BER) for$L$-ary pulse position modulation (LPPM). Then, we propose a staircase code-ordered statistics decoder (SC-OSD) algorithm for staircase low density parity check (LDPC) codes under synodic period FSO channel, and derive the probability density function (PDF) and Gaussian approximation of the ordered statistics of SC-OSD algorithm, which provide a guideline to derive lower bound of block error rate (BLER) and reduce the decoding complexity of SC-OSD. Furthermore, we propose a soft OSD-sliding window decoding (SOSD-SWD) algorithm for staircase LDPC codes under scintillation states to address the error floor problem in conventional SWD decoder. Simulation results validate that the proposed SOSD-SWD algorithm can achieve much lower BER than the related algorithms in all scintillation conditions, and shed light to tradeoff the achievable BER and complexities under different scintillation conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".