Source symbol decisions in the presence of space and time varying shallow water acoustic response functions
Bibliographic record
Abstract
Effective underwater acoustic communications requires source symbol decisions in the presence of an uncertain space and time varying acoustic response function. A hierarchical mixture Gaussian model is useful for modeling both the sparsity of arrivals as well as their spread in angle, Doppler and propagation delay [Canadian Acoust. 40]. In this framework, the delay spread, the degree of sparsity, and the Doppler spread all must be marginalized based on the observed data. We discuss the degree of sparsity as well as the correlation among multi-path arrival times and how these uncertain features in the response function can be efficiently treated in a computationally reasonable and statistically efficient manner via the hierarchy. The approach relies on iterative coherent symbol decisions with an empirical Bayes approach to estimating the hyper-parameters of the model permiting flexibility to adapt to environmental conditions. It is shown that coherent multi-path combining and Doppler compensation are possible at extremely low signal to noise ratios (i.e., < −18 dB), at ranges in excess of 1 km and with throughputs exceeding 100 bps with single element reception. Results are shown for large bandwidth M-ary orthogonal sequences tailored only to a maximum allowable multipath spread.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".