CRB Optimization for Integrated Sensing and Communication Systems Using Hybrid Linear-Nonlinear Precoding
Bibliographic record
Abstract
This paper proposes a waveform design technique for integrated sensing and communication (ISAC) systems based on hybrid linear-nonlinear precoding (HLNP). To obtain accurate direction of arrival (DOA) estimation and satisfactory waveform ambiguity properties, we optimize the weighted sum of the Cramer-Rao bound (CRB) of DOA estimation and waveform similarity, subject to constraints on the SINR of each communication user. In addition to constraints on the total power and per antenna power of the transmitted signal, we also constrain the peak to average power ratio (PAPR) on each antenna. We deploy successive convex approximation (SCA) to solve the resultant nonconvex problem while leveraging feasible point pursuit SCA (FPP-SCA) to provide a feasible initial point for the SCA algorithm. To reduce the computational cost of waveform design, we introduce a sub-block design technique. Simulation results verify the effectiveness of the HLNP algorithm and its extension, and validate their superiority over the conventional nonlinear precoding (NLP) scheme.
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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 | 0.001 |
| 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) | 0.001 | 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".