Near-Field ISAC: Beamforming for Multi-Target Detection
Why this work is in the frame
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Bibliographic record
Abstract
This article develops multi-target detection in near-field (NF) integrated sensing and communication (ISAC) systems. Specifically, the base station (BS) operates in full-duplex mode to sense the environmental information from the targets while communicating with the users. To minimize BS transmit power and to satisfy communication and sensing rate targets, we design optimal transmit beamforming (for communication and sensing) and reception beamforming at the BS. We develop an iterative beamforming algorithm to solve the resulting non-convex optimization problem. Compared to the traditional far-field benchmark, the proposed NF approach with 255 BS transmit and reception antennas uses ~1118(or ~6m) less BS transmit power to satisfy the required rate requirements. Furthermore, our proposed approach provides precise multi-target location estimates, emphasizing the advantages of NF sensing.
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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 | 0.000 |
| 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 it