Channel Quality Prediction for Adaptive Underwater Acoustic Communication
Bibliographic record
Abstract
In this paper, the communication quality of an underwater acoustic link between two nodes is quantified by the predicted channel gain and delay spread using a stochastic and reinforcement learning model. The stochastic model generates an ensemble of time-varying channel characteristics by capturing the effect of known environmental changes including changes in sound speed profile, tides and bathymetry. Along with the stochastic model to capture the impact of unknown environmental parameters on channel quality a hidden Markov model is utilized to complement sparse channel measurements and predict the channel characteristics over a long time period spanning multiple days. In this work, the nodes are bottom mounted in a shallow turbulent water environment, with known tide cycles, physical oceanography conditions and channel geometry. As such, the channel characteristics can be estimated using a simulation software model at the remote nodes. While the simulation model is used to estimate the initial channel condition between the nodes in short-term deployment, as will be shown, the hidden Markov model provides an accurate channel characteristics prediction for long term deployment, which can be utilized by software-defined acoustic nodes such that they can adapt to the time varying acoustic channel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".