Predictive Beamforming Approach for Secure Integrated Sensing and Communication With Multiple Aerial Eavesdroppers
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) is an emerging technique to enable radar and communication systems deployment on a shared hardware, channel characteristics, signal processing methods, etc. This integration improves the deployment efficiency and requires more sophisticated resource allocation and optimization techniques. The ISAC signal is designed to sense targets and to carry private information which could be at risk of being eavesdropped. This paper considers an ISAC framework in which a set of aerial eavesdroppers poses the threat of intercepting the downlink communication from a base station to a set of users. The eavesdroppers are moving, and their unknown locations are estimated based on the echo signal. A maximum likelihood-based scheme is developed to estimate the eavesdroppers’ channels, including coarse estimation with refines to estimate each eavesdropper’s complex channel gain, elevation and azimuth angles. The corresponding Cramér-Rao lower bounds of the estimated parameters are also provided. Given that the eavesdroppers are moving, a long short-term memory (LSTM) deep network is employed to predict their channels and also to enable a less frequent estimation process. Meta-learner LSTM is also presented to provide few-shot learning and provide generalization capability to any trajectory with a few fine-tuning steps. Based on the predicted eavesdroppers’ channels, two secure precoding algorithms are developed based on successive convex approximation and zero forcing techniques to improve the sum secrecy rate for the users. Simulation results illustrate that the developed framework provides substantial improvement in communication secrecy when compared with other benchmark approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".