Waveform Design for Integrated Sensing and Communications With PAPR Constraint
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Bibliographic record
Abstract
This letter investigates the peak-to-average-power ratio (PAPR)-controllable waveform design problem in an integrated sensing and communication (ISAC) system, aiming to minimize downlink multi-user interference energy and maximize the detection probability of multiple-input multiple-output (MIMO) radar. The waveform design is formulated as a weighted optimization problem based on signal similarity to achieve a flexible trade-off between sensing and communication performance. The problem is non-convex, and we thus propose an iterative waveform design algorithm. We validate the effectiveness of our proposed scheme by comparing it with benchmark strategies for communication and sensing performance, demonstrating that our approach provides significant advantages in both communication and radar performance, as well as a flexible trade-off between the two functions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it