Performance and Design of Coherent and Differential Space-Time Coded FSO Systems
Bibliographic record
Abstract
Free-space optical (FSO) communication enables high rate data transmission over the atmospheric channel. However, turbulence-induced fading poses severe challenges for operating these systems. Employing spatial diversity has been proposed as an effective measure to improve system performance. In this paper, we first develop a comprehensive model for multiple-input multiple-output (MIMO) coherent and differential FSO systems with heterodyne detection taking into account all relevant signal and noise terms. For the sake of comparison, a similar model is also developed for direct detection. An asymptotic performance analysis is presented for MIMO FSO systems employing coherent and differential space-time codes (STCs) over Gamma-Gamma fading channels. We consider the practically important case of two transmit and an arbitrary number of receive apertures and provide a simple STC design criterion for coherent and differential FSO systems. Our results reveal that, in contrast to FSO systems with intensity modulation and direct detection (IM/DD), for coherent and differential FSO systems, orthogonal space-time block codes previously introduced in the RF literature, are preferable over repetition STCs, which do not yield full diversity. We quantify the asymptotic performance gain of coherent space-time coded FSO systems compared to IM/DD systems and show that this gain is mainly caused by the superiority of heterodyne detection compared to direct detection rather than the different STC and modulation designs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".