Angular MIMO for Underwater Wireless Optical Communications: Link Modeling and Tracking
Bibliographic record
Abstract
Angular imaging multiple-input--multiple-output (A-MIMO) is investigated for short-range, high-speed underwater wireless optical communications (UWOCs) where, unlike conventional imaging MIMO (C-MIMO), data are transmitted in an angle rather than in space. In this approach, the strict requirements of on-axis alignment and fixed channel length are relaxed. This technique also allows for simpler estimation of the relative misalignment between the transmitter and the receiver from the received image. For the first time, we derive a comprehensive model for the underwater A-MIMO link by taking into account link misalignment, background noise, as well as seawater absorption and scattering. Power distributions at the receiver are modeled by the angle of arrival of the received signal on the lens and its position of arrival on the focal plane of the detector. We further propose and model a tracked A-MIMO (TA-MIMO) system that maintains the alignment between the two ends of the link, for which the distribution of the residual tracking error is calculated. The UWOC channel capacity is then estimated for buoyed-to-fixed (B2F) (which has dominant angular misalignments) and mobile-to-fixed (M2F) (which has dominant off-axis misalignment) communication scenarios. Numerical results indicate that in the B2F scenario, A-MIMO is sensitive to angular misalignments; however, TA-MIMO outperforms C-MIMO. In the case of M2F links, A-MIMO greatly outperforms C-MIMO when off-axis misalignments are present. This work serves as a design guide to determine the selection of A-MIMO, TA-MIMO, or C-MIMO receivers depending on the misalignment conditions for a particular underwater application.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".