Weighted SNR Optimization for Underwater Acoustic Integrated Sensing and Communication Based on OCDM
Bibliographic record
Abstract
The convergence of high-resolution sensing and reliable communications is the cornerstone of next-generation ocean monitoring systems. Underwater acoustic channels are notoriously hostile due to severe multipath, long delay spreads and frequency-selective fading. Orthogonal chirp division multiplexing (OCDM) has recently emerged as a robust modulation for such channels because the Fresnel transform effectively diagonalizes the convolutional channel. In this work the received OCDM block is equalized in the frequency domain using a zero-forcing ($\mathbf{Z F}$) filter, which removes both the channel and the per-subcarrier power weighting and yields a constant output signal-to-noise ratio (SNR) across chirp subcarriers. In parallel, integrated sensing and communication (ISAC) has become a key research direction for 6 G networks, promising spectrum-efficient joint sensing and communication functionality. Yet the combination of OCDM with ISAC in underwater environments remains largely unexplored. This paper presents an OCDM-based underwater ISAC framework that incorporates a novel diagonal power-allocation matrix$D_{p}$to weight each chirp subcarrier and formulates a power-allocation problem that maximizes a weighted sum of the communication and sensing SNRs. Closed-form expressions for both SNRs are derived: the communication SNR after ZF equalization equals the harmonic mean of the per-subcarrier SNRs, whereas the sensing SNR is a weighted sum of the allocated powers. We show that the resulting weighted optimization reduces to a convex program in the power variables and derive a square-root water-filling solution. Numerical simulations demonstrate that our design offers a flexible trade-off between communication and sensing performance under realistic acoustic channels and outperforms equal-power allocation benchmarks.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".