Performance Analysis of Underwater Acoustic Communications in Barrow Strait
Bibliographic record
Abstract
The Arctic Ocean and its various shallow passages are rapidly changing due to global warming. The development of a large-scale wireless network that collects and distributes oceanographic data remotely would be an extremely valuable asset to researchers. This concept, however, is completely dependent upon the ability to acoustically transmit digital information under water over long ranges, which is not currently available. In this work, we investigate the performance of four transceivers that are based on frequency-hopping binary frequency-shift keying and multilevel phase-shift keying (M-PSK) signaling in the 300$-$400-Hz acoustic band. The transceivers use low-rate coding that provides bit rates from 1.8 to 13.3 b/s (bits per second). Their bit error rates are computed based on real data recorded in Barrow Strait (NU). During the 2019 experimental activities, the transmitter was towed by a supporting vessel at an average speed of 4 kn. The receiver, a vertical hydrophone array, was at distance between 14 and 33 km. Additionally, the received signals experienced extended multipath propagation and strong in-band impulsive interferences. For a single-hydrophone receiver, our postprocessing analysis shows reliable bit rates up to 6.7 b/s. When processing uses the array and performs multichannel decision feedback equalization (DFE), the maximum designed rate, i.e., 13.3 b/s, is confirmed. Further analysis reveals that the experimental links could support higher data rates. Our results demonstrate that 2-PSK signaling combined with single-channel DFE and 4-PSK signaling combined with multichannel DFE could reliably achieve 100 and 200 b/s, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".