Cross-layer Design for Software-defined Underwater Acoustic Networking
Bibliographic record
Abstract
In this paper, a network relying on software-defined underwater acoustic nodes is proposed: by receiving a periodic beacon signal from the sink node, each node in a cluster obtains a prediction about the communication quality of the potential links to its one-hop neighbors as well as to the available relay nodes within the cluster. A hidden Markov process is used to predict the next state of the channels, using the probability distribution of the channel amplitude and delay spread from previous observations. Each transmitting node evaluates the quality of links to its next hop neighbor relay nodes. Accordingly, a normalized weight representing the channel quality is assigned to each link. The channel gain and its delay spread are the two metrics that are used to define the channel quality. To evaluate the network performance in realistic conditions, the output of a statistical model combined with the Bellhop ray tracing software is compared to experimental data. Using the channel quality, each transmitter node in the network selects its next hop optimum relay node. This scheme minimizes the number of transmitted control packets and also reduces the re-transmission of data packets, by predicting the channel status rather than exchanging an excessive number of control packets which typically convey expired channel state information. Minimizing the overhead and selecting the optimum channel saves on the energy per bit consumption while maintaining high packet delivery ratio and low latency.
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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 | 0.001 |
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".