High-frequency underwater acoustic communications using FH-FSK signaling in a reverberant shallow water environment
Bibliographic record
Abstract
This paper describes the experimental results of frequency-hopped frequency-shift-key (FH-FSK) signaling operated at 20 kHz with a 4 kHz bandwidth for underwater acoustic communications in a reverberant shallow water environment. The data were collected during the RDS4 (Rapidly Deployable Systems) experiment in a shallow water (<80 m depth) near Halifax, Canada. The measured impulse response function showed multipaths lasting over a second, which is an order of magnitude longer than the symbol length. Time-varying Doppler shifts of 30–70 Hz were found in the data. The long multipath delay and high Doppler shift are found to have a significant impact on data processing. For example, using conventional processing that detects the symbol energy over the symbol duration, the bit error rates (BER) are of the order 30–40%. Using a longer time window allowing integration of multipath energy and using Doppler estimated from trigger signals, the uncoded BER is reduced to 10–15%. The data are error-free after error decoding using a convolutional code with a rate and constraint length of 9. Consequences for acoustic networking will be discussed. [Work supported by ONR.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".