Using Vertically Separated MIMO in Ship-to-Ship Communications
Bibliographic record
Abstract
It is well established that the performance of communication systems is improved when deploying multiple antennas at one or both of the transmit/receive sides; and the amount of the gained improvement depends on the level of multipath richness of the propagation channel. In ship-to-ship overwater channels, quantifying such an improvement is not an easy task due to the effect of evaporation duct on imposing complex range- and height-dependent patterns of the received signal level. In this study, based on the parabolic equations (PE) method and using realistic evaporation duct distributions, we conduct extensive simulations in order to quantify the link-level improvement achieved when using$2\times 2$vertically-spaced Multiple-Input Multiple-Output (MIMO) systems against$1\times 2$Single-Input Multiple-Output (SIMO) and Single-Input Single-Output (SISO) systems. Then, we analyze the implication of such link improvement on the performance of a system comprised of a network of randomly distributed ships. When evaluating the outage throughput at the$2^{nd}$percentile using realistic system parameters, it was found that$2\times 2$MIMO-MRC (maximum ratio combining) systems with 1 m antenna spacing are able to improve the outage throughput by three-fold compared to SISO systems. This improvement increases to one order of magnitude when the antenna spacing increases to 10 m. It was also found that, in all cases, assuming using the same vertical spacing,$1\times 2$SIMO-MRC systems capture about 60% of the improvement achieved by$2\times 2$MIMO-MRC systems. On the other hand,$1\times 2$SIMO-DIV (diversity combining) systems are very sensitive to antenna spacing, and when assuming using the same vertical spacing, they can capture from 20% and up to 55% of the improvement achieved by$2\times 2$MIMO-MRC systems if the antenna spacing increases from 1 m to 10 m, respectively.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".