Fast multiple-symbol detection for free-space optical communications
Bibliographic record
Abstract
In this paper, we investigate noncoherent detection, i.e. detection assuming the absence of channel state information at the receiver, of on-off keying in an intensity modulation and direct detection (IM/DD) free-space optical (FSO) system. 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 several bit intervals. Specifically, we develop a fast search algorithm for optimal MSD and propose a reduced-complexity decision metric suitable for suboptimal MSD; performance results confirm that the optimal and suboptimal metrics perform comparably well. Significantly, the complexity of our receiver, on a per bit-decision basis, is only logarithmically dependent on the observation window size. We also present the framework for a decision-feedback receiver and obtain performance expressions for the ideal case of error-free feedback; these expressions serve as an upper bound to the performance of MSD. Analytical and simulation results indicate that as the observation window size increases, the performance of the MSD receiver approaches that of detection with channel state information. The conclusion is reached that the proposed implementation provides an attractive low-complexity mechanism for performing noncoherent MSD in FSO systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".