Power Allocation for SNR Optimization in OCDM-Based Underwater Acoustic Communications
Bibliographic record
Abstract
Orthogonal chirp division multiplexing (OCDM) has emerged as a robust alternative to orthogonal frequency‑division multiplexing (OFDM) for challenging underwater acoustic (UWA) channels. By spreading each information symbol over the entire spectrum via the discrete Fresnel transform, OCDM offers enhanced resilience to frequency‑selective fading and Doppler shifts. Most existing OCDM works assume uniform power allocation across chirp subcarriers. In this paper we introduce a diagonal power‑allocation matrix between the discrete Fourier transform (DFT) and the chirp modulation to weight individual chirp subcarriers and optimize communication performance. Power‑allocation problems are formulated to maximize the signal-to-noise ratio (SNR) for two receiver architectures—zero forcing (ZF) and minimum‑mean‑square‑error (MMSE) equalizers—under a total power constraint. Numerical results over simulated UWA channels demonstrate that the optimized power‑allocation schemes yield substantial bit‑error‑rate (BER) improvements compared to uniform power allocation, illustrating the relative benefits in ZF and MMSE equalizers.
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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 | 0.001 | 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 | 0.000 |
| 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".