The Spatial Correlation of a Multiple-Input Multiple-Output and Channel Model using Huygens-Fresnel Principle for Underwater Acoustic
Bibliographic record
Abstract
In this work, the spatial correlation of a multiple-input multiple-output (MIMO) for underwater acoustic (UWA) channel is modelled. To obtain the spatial correlation for such a channel, a mathematical method to model the effect of the surface on the acoustic propagation is studied. The sea surface has a significant impact on the underwater acoustic propagation (UWA) channel since the sound field is scattered, particularly in rough sea conditions. In a situation where the sea surface is calm, the reflection is specular. In contrast, a sea surface subject to high sea states generates scattered waves. In these conditions, more complex mathematical equations are required to model the propagation. Current analytical models have limitations in terms of complexity and are not practical. Therefore, this study aims to consider a specular reflection to model the time-varying sea surface on the UWA channel. It is a simple model with low computationally complexity and can be used to assess the performance of UWA communications. Specifically, the specular reflection and transmission of an acoustic wave at a calm sea surface is studied, using the Huygens-Fresnel principle and the superposition theorem. The analytical model is developed using physical oceanic parameters representing the sea conditions. The results show a good agreement with the experimental analysis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".