A Recursive Gaussian Newton Based Variable Parameter Adaptive Receiver for Single-Carrier Underwater Acoustic Communications
Bibliographic record
Abstract
A direct adaptive equalizer (DAE) combined with a digital phase-locked loop (PLL) is a widely used receiver scheme for single-carrier underwater acoustic (UWA) communications. Ever since its birth, numerous ameliorated DAE+PLL schemes have been proposed, including sparsity-aware schemes and variable parameter (VP) schemes. The sparsity-aware designs have been extensively investigated. In contrast, the VP-DAE+PLL schemes received very little attention. Under harsh channel conditions, e.g., communication channels among moving unmanned underwater vehicles (UUVs), it is necessary to employ dynamic parameters so as to achieve robust performance. In this paper, we propose a new VP-DAE+PLL scheme in which the VP is achieved via a recursive Gaussian-Newton (RGN) algorithm instead of conventional stochastic gradient descent (SGD) method. At-sea experimental results are provided to verify the advantages of the proposed RGN-VP-DAE+PLL scheme. It showed the proposed scheme achieves better performance than existing SGD-based VP-DAE+PLL schemes with a slight complexity overhead. Moreover, it is less sensitive to initial parameter setting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".