Multiple-symbol detection for photon-counting MIMO free-space optical communications
Bibliographic record
Abstract
We employ a photon-counting signal model of a multiple-input multiple-output (MIMO) free-space optical (FSO) system and investigate detection assuming the absence of channel state information (CSI) at the receiver, in moderate to strong atmospheric turbulence. The considered modulation format is on-off keying with repetition coding across the transmitters. To partially recover the performance loss associated with symbol-by-symbol detection without CSI, we consider the application of multiple-symbol detection (MSD) to equal gain combined (EGC) statistics. We develop a fast search algorithm for EGC-MSD and propose a suboptimal closed-form decision metric suitable for reduced-complexity implementation; performance results confirm that the true 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 length N, and is effectively independent of the size of the MIMO array. 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 EGC-MSD. Analytical and simulation results indicate that the system effectively realizes the diversity gains expected from a MIMO configuration and that the performance of the EGC-MSD receiver approaches the EGC with CSI lower bound with increasing N.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".