Coherent Wireless Optical Communications With Predetection and Postdetection EGC Over Gamma–Gamma Atmospheric Turbulence Channels
Bibliographic record
Abstract
Wireless optical communication systems with coherent detection are analyzed for Gamma–Gamma distributed turbulence channels. In addition to the shot noise, we consider the impacts of both turbulence amplitude fluctuations and phase fluctuations on the error performance. Error rate analyses of predetection and postdetection equal gain combining (EGC) are carried out. We derive the exact error rate expressions for predetection and postdetection EGC using a characteristic function method. In the case of predetection EGC, we also study the impact of phase noise compensation error on the error rate performance. It is shown that the error rate performance of predetection EGC is sensitive to phase noise compensation errors for both weak and strong turbulence conditions. In order to alleviate the impact of phase noise, postdetection EGC with differential phase-shift keying is introduced and analyzed. In addition, postdetection EGC is compared with predetection EGC in the presence of phase noise compensation errors, and it is found to be an effective alternative to predetection EGC with low complexity implementation.
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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) | 0.001 | 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 | 0.000 |
| 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".