Message passing-based single-carrier communications in deep-sea horizontal acoustic channels: Joint interference cancellation and symbol detection
Bibliographic record
Abstract
Deep-sea horizontal acoustic channels are characterized by widely separated clusters, with inter-cluster intervals spanning several hundred milliseconds. This channel type leads to severe inter-block interference (IBI) in zero-padded single-carrier block transmissions. In this paper, we propose a two-step equalizer based on joint channel estimation (CE), IBI cancellation (IBIC), and symbol detection (SD), termed JCE-IBIC-SD. The proposed equalizer effectively suppresses IBI through iterative CE and symbol detection, even with insufficient training sequences. In the first step, considering the large separation between multipath clusters, the equalizer decomposes deep-sea channels longer than one block into quasi-synchronous channels and retrieves the symbols for all blocks through IBIC and equalization. In the second step, based on the a priori knowledge of the symbols, the JCE-IBIC-SD is applied. Using the damped Gaussian generalized approximate message passing sparse Bayesian learning algorithm, iteratively computes the maximum a posteriori estimates of both the channels and symbols through within a joint factor graph model. Simulations and deep-sea experimental results demonstrate that, even when the receiving array is located in the acoustic shadow zone, the proposed equalizer outperforms traditional IBIC equalizers with reasonable computational complexity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".