The Effect of Directional Ambient Noise on an Underwater Acoustic Link in Shallow Environments
Bibliographic record
Abstract
To evaluate the performance of underwater acoustic communication systems, it is typically assumed that the noise at the receiver is uncorrelated spatially and temporally. This assumption underestimates the impact of acoustic ocean ambient noise on the performance of communication systems. In this article, the impact of ocean ambient noise on a coherent acoustic communication system is analyzed. The communication performance is assessed in narrowband conditions at a center frequency of${\text{2.048}\;\text{kHz}}$using the noise measurements from two different experiments—DalComm1 and the shallow water Canada Basin acoustic propagation experiment (SW CANAPE). DalComm1 focuses on characterizing the acoustic channel over ranges of 1–10 km on the Nova Scotian littoral, while the spatio-temporal variability of noise propagation in shallow and deep water environments was characterized during the CANAPE experiments. The ambient noise coherence and directionality in both environments were also measured. Two distinct noise modeling methodologies are presented to represent realistic synthetic ambient noise with defined directionality. Further, the synthetic noise is validated against measured ambient noise. The impact of ambient noise characteristics on an optimum space-time filter is characterized. A frame structure with an optimum training duration is also defined for the adaptive filter. It is observed that the bit-error rate of the space-time filter depends on optimizing the training and payload duration in the received signal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".