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 (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Z F}$</tex>) 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D_{p}$</tex> 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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".