A Simulation Study for Long-Range Underwater Acoustic Networks in the High North
Bibliographic record
Abstract
In stark contrast to a typical underwater acoustic network (UAN) deployed in mid-latitudes, ice-covered environments make network deployment difficult and expensive. A limited number of nodes must cover ranges of hundreds of kilometers. We tackle the network design in three layers: engineering, physical, and networking. At the engineering layer, we investigate hardware and bandwidth limitations for real-world implementation. Based on the proposed bandwidth, we design a software modem equipped with three waveforms achieving 1.8, 21.4, and 96.2 b/s. The packet error rate performance is computed with a channel simulator that takes realistic environmental parameters. Our simulations show that ranges of more than 100 km can be achieved in two High North areas during summer months provided that the point-to-point links exploit the ducted sound propagation. However, during winter months, this performance may not be always possible and multiple hops may be needed to cover the same range. Finally, based on the outcomes of the physical layer, an adaptive cross-layer routing protocol, termed network-aware adaptive routing (NADIR), is simulated. Link quality, energy consumption, and topological data are used to select the best coded modulation scheme and relay node in the next transmission slot. Our results show that the use of an adaptive strategy offers higher packet delivery and lower energy consumption than a nonadaptive strategy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".