Synchronization, Identification, and Signal Detection for Underwater Photon-Counting Communications With Input-Dependent Shot Noise
Bibliographic record
Abstract
Photon counting (PhC) is an effective detection technology for underwater optical wireless communication (OWC) systems. The presence of signal-dependent Poisson shot noise and asynchronous multi-user interference (MUI) complicates the processing of received data signals, hindering the effective signal detection of PhC OWC systems. This paper proposes a novel iterative signal detection method in grant-free, multi-user, underwater PhC OWC systems with signal-dependent Poisson shot noise. We first introduce a new synchronization algorithm with a unique frame structure design. The algorithm performs active user identification and transmission delay estimation. Specifically, the estimation is performed first on a user group basis and then at the individual user level with reduced complexity and latency.We also develop a nonlinear iterative multi-user detection (MUD) algorithm that utilizes a detection window for each user to identify interfering symbols and estimate MUI on a slot-by-slot basis, followed by maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-posteriori</i> probability detection of user signals. Simulations demonstrate that our scheme achieves bit error rates comparable to scenarios with transmission delays known and signal detection perfectly synchronized.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".