Adaptive Error-Correction Coding Scheme for Underwater Acoustic Communication Networks
Bibliographic record
Abstract
Underwater acoustic communication networks (UWANs) have recently attracted much attention in the research community. Two properties that set UWANs apart from most radio-frequency wireless communication networks are the long propagation delay and the possible sparsity of the network topology. This in turn offers opportunities to optimize throughput through time and spatial reuse. In this paper, we propose a new adaptive coding method to realize the former. We consider time-slotted scheduling protocols, which are a popular solution for contention-free and interference-free access in small-scale UWANs, and exploit the surplus guard time that occurs for individual links for improving transmission reliability. In particular, using link distances as side information, transmitters utilize the available portion of the time slot to adapt their code rate and increase reliability. Since increased reliability trades off with energy consumption per transmission, we optimize the code rate for best tradeoff, considering both single and multiple packet transmission using the incremental redundancy hybrid automatic repeat request (IR-HARQ) protocol. For practical implementation of this adaptive coding scheme, we consider punctured and rateless codes. Simulation results demonstrate the gains achieved by our coding scheme over fixed-rate error-correction codes in terms of both throughput and consumption of transmitted energy per successfully delivered packet. We also report results from a sea trial conducted at the Haifa harbor, which corroborate the simulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".