Channel Measurement and Markov Modeling of an Urban Free-Space Optical Link
Bibliographic record
Abstract
Free-space optical (FSO) communication links provide high data rates; however, their reliability is heavily dependent on weather conditions. This paper presents our experimental urban 1.87 km FSO link based on a customized commercial system and develops a library of channel measurements in clear and light rain weather conditions. A channel model for the link is proposed and experimentally quantified. Channel measurements are obtained by modulating a 60 mW laser source. At the receiver, a 2 GSa/s data converter is used and 16 fast-Fourier transform cores are implemented in the hardware to improve noise immunity. The resulting signal-to-noise ratio of the channel samples is around 40 dB under clear weather conditions. Fittings with log-normal, gamma–gamma, and Erlang distributions are presented, and the scintillation index and coherence time are measured. A computationally efficient finite-state Markov chain is derived for the channel to model both the distribution and the autocorrelation of the fading and is verified by the measurements. The Markov models and channel measurements in a variety of atmospheric conditions are available for download to permit easy verification of communication algorithms on this urban FSO channel.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".