BATS Coding for Underwater Acoustic Communication Networks
Bibliographic record
Abstract
Communication networks formed by Autonomous Un- derwater Vehicles (AUVs) have recently been employed for a variety of applications in oceanographic exploration and environmental protection, and military uses. The promise of such networks is very high, but their performance is limited by the low reliability of communications over the underwater acoustic channel. In order to increase the end-to-end throughput in multi-hop network configurations, chains of relay nodes can be employed. Unfortunately, the usual techniques utilized to address channel errors and packet erasures in multi-hop terrestrial networks such as Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) coding are ineffective in the underwater channel due to adverse multipath propagation and long delays. Solutions based on network coding have, therefore, been investigated in the literature. We propose and evaluate utilization of Batched Sparse (BATS) coding, which is a combination of fountain coding and network coding, in order to improve the resilience of multi-hop underwater acoustic communication networks (UWACNets) consisting of multiple underwater transceiver nodes. BATS coding allows to overcome the limitations of channel coding in terms of robustness and those of network coding in terms of computational complexity, while noticeably improving throughput performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".