Multiple-Symbol Detection for Photon-Counting Free-Space Optical Communications
Bibliographic record
Abstract
Free-space optics (FSO) have received increased attention recently for last-mile, line-of-sight wireless links. In this paper, we employ a photon-counting signal model of an FSO system and investigate noncoherent detection, i.e. detection assuming the absence of channel state information at the receiver, of on-off keying (OOK). To partially recover the performance loss associated with symbol-by-symbol noncoherent detection, we consider the application of multiple-symbol detection (MSD), in which block-wise decisions are made using an observation window of N bit intervals. We develop a fast search algorithm for optimal MSD, which corresponds to a fixed bit-wise implementation complexity effectively independent of N. Furthermore, we obtain performance expressions for an ideal decision-feedback receiver, which serve as an upper bound to the performance of the MSD receiver. Analytical and simulation results indicate that the performance of the MSD receiver approaches the coherent detection lower bound with increasing N.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".