Free-Space Optical Communication System Using Non-mode-Selective Photonic Lantern Based Receiver With Different Number of Single-Mode Fiber Cores
Bibliographic record
Abstract
The optical receiver based on non-mode-selective photonic lantern (NMS-PL) can be used to improve the communication performance of free-space optical communication (FSOC) systems, because the NMS-PL receiver can take advantages of the high coupling efficiency of multimode fiber (MMF) receivers and the high mixing efficiency of single-mode fiber (SMF) receivers. However, previous studies on the NMS-PL receiver did not consider the impact of the number of SMF cores of the NMS-PL on the bit-error rate (BER) performance under different power distributions of the NMS- PL. In this paper, we study the BER of the NMS-PL receiver using equal-gain combining (EGC) for FSOC systems under a log-normal turbulent fading channel with pointing errors. We derive both a lower bound and an approximated upper bound of the BER of the NMS-PL receiver using EGC. Numerical results show that the BER of NMS-PL receiver attains its minimum value when the number of SMF cores equals the number of guided modes of NMS-PL. Besides, numerical results also show that the power distribution of the NMS-PL has only limited influence on the BER of NMS-PL receiver using EGC when either strong turbulence or large pointing error is considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".