Acoustic propagation properties of underwater communication channels and their influence on the medium access control protocols
Bibliographic record
Abstract
Underwater acoustic communications in the ocean is complicated as the acoustic signals may be attenuated, distorted and delayed. In this paper, we review the underwater acoustic signal propagation properties in terms of sound speed profile, spreading loss and absorption loss. We study and compare different approaches on the calculation of signal transmission loss in the water, more specifically, the ray theory model approach and the semi-empirical formula approach. Using the Acoustic Toolbox, we compare their performance under different environmental parameters, including the sound source depth, bathymetry data, and the horizontal distance between the sound source and receiver. Furthermore, in order to obtain how the acoustic propagation characteristics will affect the performance of medium access control (MAC) protocol, we adopt pure ALOHA protocol and use network simulator ns-2 to study the throughput performance under both shallow water and deep ocean conditions. Our results indicate that the transmission loss in the shallow water is close to the result of semi-empirical formula with transition region (k = 1.5), which is close to the result of semi-empirical formula with spherical spreading loss (k=2) in deep water.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".