Signal Enhancement and Suppression Schemes for Bi-Static ISAC With IRS-Mounted Target
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Bibliographic record
Abstract
Integrated sensing and communication (ISAC) has evolved as a critical paradigm to enhance the dual functions concurrently. However, ISAC may encounter performance limitations, due to undesired channel conditions, small target size, and security threats. In this paper, we investigate intelligent reconfigurable surface (IRS)-aided bi-static ISAC networks, where the IRS is mounted directly on the target surface, and analyze the signal enhancing and suppressing effects of the target-mounted IRS, respectively. First, we maximize the sensing signal-to-noise ratio (SNR) while satisfying the users’ communication requirements by jointly optimizing the transmit beamforming and IRS reflection. To solve this optimization problem, an alternating optimization algorithm is employed to decouple the optimization variables, followed by the application of successive convex approximation and penalty dual decomposition to solve the subproblems. Second, we consider two threatening scenarios where two adversarial base stations (BSs) intend to capture the information reflected by the target. In the first scenario where the adversarial receiving BS attempts to exploit the reflected ISAC signal, we minimize its received power via optimizing the transmit beamforming and the IRS reflection alternately. In the second scenario where the adversarial transmitting BS emits a dedicated signal to detect the target, we focus on optimizing the IRS reflection. Simulation results are presented to show the effectiveness of the proposed schemes.
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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 | 0.000 | 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