Tuning the demodulation frequency based on a normalized trajectory model for mobile underwater acoustic communications
Bibliographic record
Abstract
Abstract We have developed a demodulator for low data rate, asynchronous frame, and narrow bandwidth underwater acoustic communication. We aim at operation under harsh conditions, ie, low signal‐to‐noise ratio, and across long distances. In this paper, we pay a special attention to the efficiency of mobility support. Mobility results into the Doppler effect, which, for a demodulator, makes the carrier frequency drift arbitrarily during attempts to decode frames. The chances of success are better when the demodulator can tune into the drifted carrier frequency. This can be achieved by trying a range of possible drifted carriers. We introduce the novel idea of normalized trajectory. Each normalized trajectory produces a unique Doppler shift pattern that can be applied to tune into a drifted carrier. We demonstrate that this improvement is theoretically sound. From a practical point of view, the search space is potentially reduced. The actual gain in performance is application‐specific and depends on the actual sets of trajectory parameters that are considered. We introduce the concept of normalized trajectory, discuss its integration into the demodulator, and review the performance of the new design.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".