Medium Access Control for Underwater Network using an SNR-Aware Adaptive Learning Agent
Bibliographic record
Abstract
In an underwater acoustic sensor network, a transmission node shares the medium with other nodes within the communication range, and due to the low speed of sound, the packets go through a long delay on the order of seconds for a network with a maximum distance of 80 km. This makes Medium Access Control (MAC) an important issue to ensure the network is operational. To deliver the data successfully while keeping the energy consumption to a minimum, a well-designed MAC protocol is required for any transmission node. In this paper, an SNR-DRAL MAC protocol incorporates deep reinforcement learning techniques to enhance network throughput in underwater acoustic networks (UANs) by reducing the likelihood of packet collisions and utilizing the non-occupied time slots from other MAC agents. The proposed DRAL-SNR MAC agent improves the network throughput and reduces frame-error-rate (FER) by including in its training phase the signal-to-noise ratio (SNR), the bit-error-rate (BER), and the frame-error-rate (FER).
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".