Low-Complexity Beamforming for NF Secure ISAC
Bibliographic record
Abstract
This letter investigates secure beamforming for a near-field (NF) integrated sensing and communication system, where an extremely large-scale antenna array (ELAA) base station (BS) serves multiple users and sensing targets under eavesdropping threats. Conventional algorithms are computationally prohibitive in large-scale scenarios. To address this, we propose a low-complexity beamforming algorithm that exploits NF beam-focusing in both angular and distance domains. The design maximizes the secrecy sum rate while satisfying the user’s SINR, target beampattern, and BS power constraints. By converting the power constraint into a complex sphere manifold, the algorithm combines manifold optimization with the augmented Lagrangian method to efficiently handle the remaining constraints. This drastically reduces the search space; for example, with 257 BS antennas, it achieves an 18-fold speedup over the convex-concave procedure algorithm (CCPA).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".