Diversity combining in FSO systems in presence of non-Gaussian noise
Bibliographic record
Abstract
Free-space optics (FSO) communication has received much attention in recent years as a cost-effective, license-free, and wide-bandwidth access technique for high data rate applications. The performance of FSO communication, however, severely suffers from turbulence caused by atmospheric conditions. Multiple photodetectors can be placed at the receiver to mitigate the turbulence and exploit the advantages of spatial diversity combining. In this paper, we analyze the bit error rate (BER) performance of an FSO communication system employing binary phase-shift keying with additive non-Gaussian noise over negative exponential distributed atmospheric turbulence and spatial diversity at the receiver. The Laplace distribution is used to model the non-Gaussian impulsive noise. We consider the case when perfect channel state information is available at the receiver for implementation of coherent detection. Analytical expressions for the BER of a single channel receiver as well as that of a diversity combining receiver using selection combining, dual-diversity equal-gain combining, and maximal-ratio combining are derived. The derived analytical expressions are verified by simulation results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".