Low-Complexity CRB Minimization for ISAC With a Generalized Target Response Matrix
Bibliographic record
Abstract
This paper presents a beamforming design for integrated sensing and communication (ISAC) systems using Riemannian manifold optimization to minimize the Cramér-Rao bound (CRB) of a generic target response matrix (TRM) for improving target estimation. CRB optimization is a challenging non-convex problem involving matrix inversion and its complex dependence on system parameters (e.g., beamforming, number of antennas, and number of targets), resulting in high dimensionality and the need to balance sensing accuracy and communication quality. Traditional solutions to these challenges, such as relaxed semidefinite programming (RSDP) and sequential convex cone optimization (SCCO), are computationally complex and have slow convergence. Thus, we propose a Riemannian conjugate iterative augmented Lagrangian manifold (RC-IALM) algorithm to minimize the TRM’s CRB while ensuring communication quality. Numerical results demonstrate its superior computational efficiency and reduced running time. For instance, it is 33 and 12 times, respectively, faster than RSDP and SCCO benchmarks when there are 24 transmit/receiver antennas at the base station.
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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 | 0.000 |
| 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".