MMSE-Lattice Sequential Equalization of Underwater Acoustic Channels
Bibliographic record
Abstract
Underwater acoustic communications is today accomplished with a variety of modulation techniques, including both non-coherent and coherent methods, each shown to perform "best" under certain environmental and operational constraints. Coherent communications typically involve a form of M-ary phase shift keyed (MPSK) signaling, combined with one of a variety of channel equalization schemes at the receiver. The most common scheme is the mean square error minimization (MMSE) decision feedback equalizer (DFE), designed in part by Professors John Proakis and Milica Stojanovic and now extended by many developers for a variety of applications. Even though often effective, the performance of the DFE becomes far from the optimal performance that can be obtained by the maximum likelihood sequence estimator (MLSE). While aiming for the optimal performance, our interest is in the use of small, battery- powered, DSP-based devices which have fairly hard constraints on computer resources. We consider an older alternative to the DFE, the maximum likelihood sequence estimator (MLSE), usually implemented with a Viterbi algorithm. Referring to historical coding theory, it is well known that a sequential estimation alternative to MLSE-based decoding is "almost" as effective, given a modest increase in signal-to-noise ratio (SNR). Furthermore, the computational burden is usually far less than with the conventional Viterbi approach. The focus of this paper is on the development and application of a Fano Sequential Estimator acting as an alternative to the Viterbi algorithm for MLSE-based channel compensation.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".